Antagonism, Agonism, and the Dynamics of Discursive Struggles in the Australian Twittersphere

Networked Discursive Alliances: Antagonism, Agonism, and the Dynamics of Discursive Struggles in the Australian Twittersphere

Ehsan Dehghan

Bachelor of Arts, Master of Arts

Submitted in fulfilment of therequirements for the degree of

Doctor of Philosophy

School of Communication

Creative Industries Faculty

Queensland University of Technology

2020 Keywords

Active passivity, Affordances, Agonism, Agonistics, Antagonism, Australian

Twittersphere, Democracy, Discourse, Discourse theory, Discursive struggle,

Discursive-material analysis, Discursive-material knot, Echo chambers, Filter bubbles, Networked discursive alliances, Platforms, Polarisation, Social media,

Twitter

Abstract

The theorisation of social media’s inter-relationships with democracy raises considerable complexities. Through their many-to-many modes of communication, social media allow for the visibility of a wide range of voices and discourses. This visibility is further amplified through the connective affordances of the platforms and their algorithms. These affordances initially seemed highly promising for democratic thought.

After more than a decade of witnessing different phenomena on social media platforms, however, we are now past this initial optimism about the positive role that platforms play in democratic projects. Especially in recent years, there has been increasing concern—and pessimism—about the role of platforms in democracies, particularly with regard to the role they can play in the polarisation of societies, the manipulation of users, and the radicalisation of extremist discourses.

Starting from the premise that the goal of democracy should be the transformation of antagonism (struggle between enemies) into agonism

(struggle between adversaries), the primary focus of the present study is to examine the dynamics involved in the formation of polarised antagonistic spaces on social media platforms, particularly Twitter. At the same time, it also focuses on the dynamics involved in bringing antagonistic communities closer, and in transforming polarisation into agonism. It does so by assuming a non-hierarchical and knotted relationship between discourse, material, structure, and agency.

This study employs a mixed-methods approach, drawing from social media analytics, social network analysis, corpus linguistics, and discourse-theoretical iii analysis to investigate three case studies, covering different socio-political issues and contexts in Australia. The first case study examines Twitter conversations around RoboDebt, a controversy arising from the automated handling and issuing of debt notices to welfare recipients. The second focuses on Australia’s Racial Discrimination Act, a highly polarising issue. Finally, the third case study investigates conversations about immigration and refugees in the Australian Twittersphere.

This research contributes to knowledge by showing that the polarisation observed on the platform is not simply due to its technological design and affordances, or to filter bubbles and echochambers. Rather, it is primarily due to users’ particular discursification ofthe materialities of the platform. While communities of users reproduce their discourses in their tweets, they strategically discursify the materialities of the platform in order to construct agonistic networked discursive alliances against their antagonists.

Concurrently, although communities of users are exposed to the discourse of the other, they remain actively passive so as to further amplify antagonisms.

The project also shows how users’ particular discursifications of the materialities of Twitter create temporary spaces in which discursive struggles can occur, even between communities of users who might otherwise be disconnected. These temporary spaces provide the necessary conditions for the construction of a communication space that is built on agonism.

Table of Contents

List of Figures...... ix List of Tables ...... x Acknowledgements ...... xiv 1 Introduction ...... 1 1.1 Background ...... 1 1.2 Research Question and Objectives ...... 4 1.3 Thesis Outline ...... 11 2 Theoretical Framework ...... 15 2.1 Discourse Theory ...... 16 2.1.1 Discourse ...... 17 2.1.2 Elements, Moments, Articulation ...... 21 2.1.3 Identity and Antagonism ...... 23 2.1.4 From Antagonism to Agonism: Chains of Equivalence ...... 24 2.2 The Discursive-Material Knot ...... 27 2.2.1 The Four Strands of a Knot ...... 29 2.3 Operationalising Discourse Theory ...... 32 2.3.1 Sensitising Concepts ...... 33 2.3.2 Sensitising Concepts of The Project ...... 35 2.4 Setting the Limits ...... 36 3 Literature Review ...... 39 3.1 Introduction ...... 39 3.2 Social Media and Democracy ...... 40 3.3 Public Sphere, Democracy, and Social Media ...... 44 3.4 The Fragments of a Broken Public Sphere ...... 48 3.4.1 A Discursive-Theoretical Re-Reading ...... 54 3.4.2 Social Media Materialities and Discourses ...... 56 3.5 Horizontal and Vertical Contexts ...... 62 3.6 Twitter: An Object of Study, and Much More ...... 68 3.6.1 Methodological Approaches to Studying Twitter ...... 71 v

3.7 The Australian Twittersphere...... 75 3.8 Some Limitations ...... 81 4 Research Design ...... 85 4.1 Introduction ...... 85 4.2 Data Collection and Preparation ...... 85 4.3 Analytical Procedure ...... 88 4.3.1 Phase 1: Temporal and Aggregate Metrics of Discourse ...... 88 4.3.2 Phase 2: Network Metrics of Articulation ...... 94 4.3.3 Phase 3: Textual Articulatory Practices ...... 102 4.4 Ethical Considerations ...... 107 5 The Three Case Studies ...... 110 5.1 Introduction ...... 110 5.2 #RoboDebt, An Algorithmic Controversy ...... 110 5.2.1 Significance for the Project ...... 112 5.2.2 Data Collection ...... 114 5.3 Section 18C and Freedom of Speech ...... 115 5.3.1 Significance for the Project ...... 118 5.3.2 Data Collection ...... 119 5.4 The Immigration Conundrum ...... 120 5.4.1 Significance for the Project ...... 126 5.4.2 Data Collection ...... 127 6 Collective Patterns of Activity ...... 129 6.1 Introduction ...... 129 6.2 Tweets over Time ...... 130 6.2.1 #RoboDebt ...... 130 6.2.2 Section 18C ...... 133 6.2.3 Immigration ...... 139 6.3 Hashtags ...... 143 6.3.1 #RoboDebt ...... 144 6.3.2 Section 18C ...... 146

6.3.3 Immigration ...... 150 6.4 Information Sources ...... 155 6.4.1 #RoboDebt ...... 156 6.4.2 Section 18C ...... 158 6.4.3 Immigration ...... 161 6.5 Visibility and Discursive Power ...... 163 6.5.1 #RoboDebt ...... 165 6.5.2 Section 18C ...... 171 6.5.3 Immigration ...... 178 6.6 Conclusions ...... 186 7 Discursive Networks of Articulation ...... 193 7.1 Introduction ...... 193 7.2 Retweet Networks ...... 194 7.2.1 #RoboDebt ...... 195 7.2.2 Section 18C ...... 199 7.2.3 Immigration ...... 201 7.3 Intra-, Inter- and Extra- Cluster Dynamics of Retweeting ...... 204 7.3.1 Section 18C ...... 207 7.3.2 Immigration ...... 210 7.3.3 Summative Remarks ...... 215 7.4 @Mention Networks ...... 216 7.4.1 #RoboDebt ...... 217 7.4.2 Section 18C ...... 221 7.4.3 Immigration ...... 223 7.5 Intra-, Inter-, and Extra- Cluster Dynamics of @Mentions ...... 226 7.5.1 Section 18C ...... 226 7.5.2 Immigration ...... 228 7.5.3 Summative Remarks ...... 231 7.6 Conclusions ...... 234 8 Textual Articulations ...... 238 vii

8.1 Introduction ...... 238 8.1.1 #RoboDebt ...... 240 8.1.2 Section 18C ...... 245 8.1.3 Immigration ...... 250 8.2 Conclusions ...... 263 9 Synthesis and Discussion ...... 268 9.1 Introduction ...... 268 9.2 Discursive-Material Articulations ...... 269 9.3 Networked Discursive Alliances ...... 276 9.4 Active Passivity ...... 285 9.5 Filter Bubbles and/or Echo Chambers ...... 288 10 Conclusion ...... 299 10.1 Thesis Summary ...... 299 10.2 Polarisation, Its Solutions, and Their Implications ...... 305 10.3 Future Directions ...... 312 References ...... 318

viii

List of Figures

Figure 2-1: 'Overview' of the discursive-material knot ...... 30 Figure 2-2: The primary and secondary sensitising concepts of discursive- material analysis ...... 34 Figure 3-1: Horizontal and vertical contexts ...... 67 Figure 3-2: The Australian Twittersphere in 2016 ...... 78 Figure 6-1: #RoboDebt tweets over time ...... 131 Figure 6-2: Section 18C tweets over time ...... 136 Figure 6-3: Section 18C tweets over time by antagonistic clusters ...... 138 Figure 6-4: Section 18C tweets over time by antagonistic clusters, normalised ...... 138 Figure 6-5: Immigration tweets over time ...... 142 Figure 7-1: Retweet network of #RoboDebt ...... 196 Figure 7-2: Retweet network of #RoboDebt with core accounts removed .. 198 Figure 7-3: Retweet Network of the 18C debate ...... 200 Figure 7-4: Retweet network of the Immigration case study ...... 203 Figure 7-5: @Mentions network of #RoboDebt ...... 219 Figure 7-6: @Mentions network of #RoboDebt with cores removed ...... 220 Figure 7-7: @Mentions network of 18C ...... 222 Figure 7-8: @Mentions network of Immigration case study ...... 225 Figure 8-1: Semantic network of keywords in the discourse of communities in the Immigration case ...... 262 Figure 9-1: Conceptualisation of multilayered discursive articulations ...... 272 Figure 9-2: Laclau's diagrammatic presentation of chains of equivalence .... 278 Figure 9-3: Conceptualisation of networked discursive alliances ...... 280 Figure 9-4: Carpentier's palm-tree model of antagonism and agonism ...... 284

List of Tables

Table 2-1: Sensitising concepts of the study ...... 36 Table 6-1: Top hashtags in #RoboDebt ...... 144 Table 6-2: Top hashtags in 18C ...... 147 Table 6-3: Top hashtags by the Progressive cluster in 18C ...... 149 Table 6-4: Top hashtags by the Hard-Right cluster in 18C ...... 150 Table 6-5: Top hashtags in Immigration ...... 151 Table 6-6: Top hashtags by the Progressive cluster in Immigration ...... 153 Table 6-7: Top hashtags by the Hard-Right cluster in Immigration ...... 154 Table 6-8: Top information sources in #RoboDebt ...... 157 Table 6-9: Top information sources in 18C ...... 158 Table 6-10: Top information sources for the Progressive cluster in 18C ..... 159 Table 6-11: Top information sources for Hard-Right cluster in 18C ...... 160 Table 6-12: Top information sources for the Progressive cluster in Immigration ...... 161 Table 6-13: Top information sources for the Hard-Right cluster in Immigration ...... 163 Table 6-14: Most active accounts in #RoboDebt ...... 165 Table 6-15: Most retweeted accounts in #RoboDebt ...... 167 Table 6-16: Most @mentioned accounts in #RoboDebt...... 168 Table 6-17: Most active accounts in 18C ...... 171 Table 6-18: Most active Hard-Right and Progressive accounts in 18C ...... 172 Table 6-19: Most retweeted accounts in 18C ...... 173 Table 6-20: Accounts receiving the highest retweets from the Hard-Right and Progressive clusters in 18C ...... 175 Table 6-21: Most @mentioned accounts in 18C ...... 176 Table 6-22: Accounts receiving the most @mentions by the Progressive cluster in 18C ...... 177 Table 6-23: Accounts receiving the most @mentions by the Hard-Right cluster in 18C ...... 177 Table 6-24: Most active accounts in Immigration ...... 179

Table 6-25: Most active Progressive accounts in Immigration ...... 180 Table 6-26: Most active Hard-Right accounts in Immigration ...... 181 Table 6-27: Most retweeted accounts in Immigration ...... 182 Table 6-28: Accounts receiving the most retweets by the Progressive cluster in Immigration ...... 183 Table 6-29: Accounts receiving the most retweets by the Hard-Right cluster in Immigration ...... 184 Table 6-30: Most @mentioned accounts in Immigration ...... 185 Table 6-31: Accounts receiving the most @mentions by the Progressive and Hard-Right clusters in Immigration ...... 186 Table 7-1: Intra- and extra-cluster dynamics of retweeting in the 18C case study ...... 208 Table 7-2: Inter-cluster dynamics of retweeting in the 18C case study ...... 208 Table 7-3: Most active discourse communities in the Immigration case study ...... 210 Table 7-4: Extra-cluster dynamics of retweeting in the Immigration case study ...... 211 ...... 213 Table 7-6: Extra-cluster dynamics of @mentions in the 18C case ...... 227 Table 7-7: Inter-cluster dynamics of @mentions in the 18C case ...... 227 Table 7-8: Extra-cluster dynamics of @mentions in the Immigration case study ...... 229 Table 7-9: Inter-cluster dynamics of @mentions in the Immigration case study ...... 231 Table 8-1: Top keywords of the Progressive cluster in the #RoboDebt case ...... 242 Table 8-2: Top keywords of the Progressive Political Commentators (Hard- Left) cluster in the #RoboDebt case ...... 244 Table 8-3: Top keywords of the Hard-Right cluster in the #RoboDebt case ...... 245 Table 8-4: Top keywords of the Progressive and Hard-Right clusters in the 18C case ...... 248 Table 8-5: Top keywords of the Charities cluster in the Immigration case . 251 xi

Table 8-6: Top keywords of the Literature cluster in the Immigration case 252 Table 8-7: Top keywords of the Lawyers cluster in the Immigration case .. 253 Table 8-8: Top keywords of the Politicians and Political Journalists cluster in the Immigration case ...... 254 Table 8-9: Top keywords of the Journalists cluster in the Immigration case ...... 255 Table 8-10: Top keywords of the Education and Science clusters in the Immigration case ...... 256 Table 8-11: Top keywords of the Progressive cluster in the Immigration case ...... 257 Table 8-12: Top keywords of the Hard-Left cluster in the Immigration case ...... 258 Table 8-13: Top keywords of the Hard-Right cluster in the Immigration case ...... 260

xii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution.

To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Signature:

QUT Verified Signature

Date:

10 Feb 2020

xiii Acknowledgements

First and foremost, I am forever and deeply indebted to my brilliant supervisory team: Axel Bruns, Peta Mitchell, and Brenda Moon. I could not have done this research without them. Their critical insights, Socratic questions, mentorship, guidance, and support played the most crucial role in forming this research. Since day one, they were there for me, not only as supervisors and educators, but as colleagues and friends.

My heartful gratitude goes to my friends and colleagues at the DMRC. Many of the conversations we had over lunch or coffee helped develop the ideas in this thesis. Special thanks to Felix Münch, Silvia Montaña, Ariadna

Matamoros-Fernández, Edward Hurcombe, Fiona Suwana, Kelly Lewis, and

Aleesha Rodriguez, for their ideas, friendship, and encouragement.

I would like to extend my sincere thanks also to Alice Witt, Emma Baulch,

Sofya Glazunova, Yi Wang, Callum McWaters, David Myles, Tim Highfield,

Guy Healy, Xu Chen, Aljosha Karim Schapals, Stefanie Duguay, Bondy Kaye,

Smith Mehta, Jarrod Walczer, Delfi Chinnappan, Michael Dezuanni, Elija

Cassidy, Timothy Graham, Kevin Sanson, and Patrik Wikström.

I am extremely grateful to Kim Osman and Stephen Harrington for their constructive feedback during my confirmation seminar, and to Daniel Angus and Ariadna Matamoros-Fernández for their valuable insights during my final seminar. I would also like to thank Denise Scott for her professional proofreading of this thesis before submission.

Many thanks go to Jean Burgess, for ensuring an inclusive, respectful, and intellectual culture at the DMRC, and for involving me in various projects and events in the centre, including our annual summer school. xiv

I also had the great pleasure and privilege of attending the Summer Doctoral

Programme at the Oxford Internet Institute (OII-SDP). I would like to recognise the role of the faculty and staff at the OII, especially the role of Vicki

Nash and the thirty friends I met during the two-week programme. My attendance was made possible through the generous John Hartley scholarship for DMRC doctoral candidates.

Financially and technologically, my research was supported by the QUT

Postgraduate Research Award (QUTPRA), Australian Research Council

Future Fellowship project, Understanding Intermedia Information Flows in the

Australian Online Public Sphere, and LIEF project, TrISMA: Tracking

Infrastructure for Social Media Analysis. I also received a top-up scholarship from the DMRC, which I greatly appreciate.

Finally, thanks to my parents, Hengameh and Naser, without whom I would not be where I am today. And most importantly, thanks to my partner

Maryam, for her love, support, and kindness. I do not have the words to thank her enough. Thanks for sharing this experience with me, and for helping me through the dark times.

1 Introduction

I believe that any scholar starting a research project does so because they are intrigued by a question that has personal roots. If someone is to dedicate years of their life to answering a question, they are likely to be personally invested in the answer. For me, this is the case with the question of social media and their role in democracy.

1.1 Background

I joined Facebook in August 2008. At the time, I loved the fact that I could connect with my friends, find old classmates from primary school, and make new virtual friends in other cities. I had also found a new space where I could easily write my opinions about different topics; I could also write my poetry there, rather than in a blog. The Persian blogosphere was so oversaturated that writing a blog was no longer ‘cool’.

Soon, however, my happiness was replaced with a feeling of frustration. Almost all my friends were playing a Facebook game that I found highly disturbing.

The game—Friends for Sale—involved players buying and selling their

Facebook friends like virtual pets. They would promote each other, make each other more valuable, and send each other virtual gifts. All the classic signs of commodification of bodies and objectification of humans—generally blamed on

Hollywood and the fashion industry—had been transferred to this game. This time, it was one’s friends (rather than some big industry) who were treating each other like commodities. And all this time, I was contemplating the banality and superficiality of interactions on the platform. Maybe I had made

Introduction 1 a mistake in shutting down my blog and moving my serious thoughts to

Facebook?

A year or so passed, and Iran—my home country—was approaching an important presidential election. My initial frustration with the platform subsided to some extent when I saw how my friends were using Facebook to have serious political discussions and debates about which candidate to vote for, whether or not to boycott the elections, and how and where to campaign for their preferred candidates.

On the day following the election, all hell broke loose on the streets of the major cities in Iran. The candidate that many had anticipated to win the election had lost by a large margin, and many believed there was election fraud involved. Protests formed on the streets. Thousands showed up, and soon the peaceful protests took a violent turn. I still vividly remember the mixed feelings of anxiety and hope as I sat at my laptop, following the news about the protests in different cities. There was no substantial news about them on the state- controlled television, the radio, or newspapers. The only sources providing the news were social media: Facebook (particularly); Twitter; Balatarin.com (a

Persian aggregator website operating in a similar way to Reddit); and

YouTube.

People were sharing the details of where to gather for the next protest, and videos of what happened on the streets and in the clashes between the protesters and the riot police. Underground artists soon created videos, songs, and poetry recitals and shared them on YouTube. Videos from the 1979 Iran revolution were repurposed, re-contextualised, and juxtaposed with what was happening on the streets, and the YouTube video of the death of Neda Agha

Soltan made the horrific incident “probably the most widely witnessed death

2 Introduction in human history” (Mahr, 2009). A year after the turmoil in Iran, several other countries in the Middle East witnessed the same phenomenon. The Arab Spring once again showed the potentials of social media platforms as amplifiers of the democratic demands of the masses. As it turned out, therefore, social media was not simply the banal phenomenon I had initially thought it to be.

In the decade after these examples, we have witnessed the many such potentials of social media platforms: Activists have used, and are using them to create collectives and find critical mass for their causes; hopeful artists use them to build a fan base; start-ups promote their products there; and politicians use them to campaign during elections.

In the same decade, we have also witnessed the darker sides of ubiquitous social media platforms: We have seen surveillance, and have experienced personal data breaches; there is doxxing; there is harassment; there is revenge porn; we have witnessed Gamergate, and we have seen Cambridge Analytica; there is the rise of extremist views; there is the election of Donald Trump and claims of online (Facebook) manipulation of voters; there are click farms and troll factories, formed to manipulate public opinion; and most recently, a terrorist live-streamed his massacre on Facebook. All these nasty sides of the human experience are played out online, and many have detrimental impacts on democracies, both well-established and emerging.

How should we make sense of the inter-relationship between social media and democracy? Should we rely more on the positive examples of the past decade, or give more weight to the negatives, especially the more recent ones? What is the role of social media in the increasing polarisation of societies? How can we direct and channel the use of social media so that we observe more of its good, and less of its bad side? These have been pressing questions for me since I

Introduction 3 started using social media, and are still questions that many others are also grappling with.

1.2 Research Question and Objectives

The present study addresses the aforementioned questions. Broadly, this research positions itself in the long line of inquiry that investigates the inter- relationships between social media platforms and democratic projects. The overarching research question informing my long-term research endeavour, therefore, is the inter-relationship between social media platforms and democracy.

However, answering big questions such as this requires starting small, at a case-based level, and going deep into the dynamics involved in multiple case studies. A synthesis of the findings gained from different cases allows for a more nuanced, comparative, and empirical insight into the bigger picture. Therefore, in the present project, I focus on a well-established democracy—Australia—on a single platform—Twitter—and I ask a smaller question:

What are the dynamics of the discursive struggles between the different and sometimes conflicting demands of users in the

Australian Twittersphere?

There are a number of key elements in the above question. First, there is the notion of users and their demands and the agency of users in voicing those demands. Second, the concept of discourse plays a central role in the question:

The agency of individuals does not come into being in isolation; rather, it is directly influenced by social structures, discourses, and contexts. Context, therefore, becomes the third important element in the above question. Finally, the medium on which these elements manifest themselves is also a matter of

4 Introduction concern for this project. Twitter, as the platform of choice for the project, plays a key role in affording certain structures and connective practices; and these can, in turn, influence the communications taking place on it.

The concept underlying all the above-mentioned elements, of course, is the notion of democracy itself, and what we mean when we talk about ‘democracy’.

There is a multitude of views about what a democracy is or should be.

Therefore, it is necessary for this research to also delineate which democratic model it refers to when investigating the inter-relationship of social media and democracy.

At its core, democracy is a political process involving the conflicting demands of the common people (demos) and decisions about how they should be governed; that is, about people’s exertion of power (kratia) over people. It might make sense, then, to focus on people (and only people) when investigating a democratic project. However, more complexities arise when different media are involved in the democratic process that unfolds between people and eventual decisions. Any medium, through its materiality, has certain affordances that influence the actions and decisions of its users. Even if a very simple medium such as a white piece of paper is used to voice the opinions of people in a democracy, it plays a role in affording certain actions:

Its spatial boundaries limit the amount of content that can be written on it; its whiteness necessitates using darker shades to write on it; and so on.

This notion becomes even more complex when the medium is not merely a politically neutral one like a white piece of paper. Social media platforms, as media in a democratic project, are not neutral: They have material affordances which enable and privilege certain types of behaviours (Bucher & Helmond,

2018); they have moderation practices which do not allow certain other

Introduction 5 behaviours (Gillespie, 2014); and they have commercial goals that require them to design the platform in ways that can maximise the profit they seek (van

Dijck & Poell, 2013). By design, major social media platforms such as Facebook and Twitter have not been created to serve political purposes (Wright, 2012).

Rather, they have been designed as money-making machines. Their affordances, therefore, have a direct influence on the process from demos to kratia, since they impose their own power, logics, discourses, and structures onto any communication taking place on them.

These complexities make it necessary for a study to adopt frameworks that can account for users and their agency; the materialities of the platform; the discourses shaping the agency of the users; and the structures and contexts in which communications occur. In order to avoid essentialist and reductionist interpretations, it is also necessary to account for all of these without privileging one over others. Additionally, and with regard to democracy—a core focus of the present project—it is necessary to engage with theories that can accommodate these factors in democratic thought. Therefore, my theoretical foundation for the present project is engagement with Laclau and

Mouffe’s discourse theory (Laclau & Mouffe, 1985). This theory starts at a social ontological level to theorise meaning-making processes (Howarth, 2000), and builds on this social ontology to conceptualise political identities and propose a theory of democracy.

Of course, it goes without saying that the route I take in this project is by no means the only way to address the questions of social media and democracy.

The interdisciplinary nature of research projects such as the present one allows for a combination of various theories to account for the different constructs of the study. However, I find discourse theory to be an ideal fit for my research

6 Introduction endeavour. This, admittedly, is partly because of my previous training in discourse studies. To a larger extent, however, I believe that discourse theory provides a comprehensive framework that accounts for all the elements involved in the primary research question of this project.

Although discourse theory has slowly found its place in media and communications studies in general, there are few digital media studies that engage with this theory; in particular, studies of social media platforms.

Discourse theory’s democratic goals are built around the notion of the ineradicability of antagonism (Sections 2.1.3 and 2.1.4). This view is particularly useful given the increasing polarisation we are witnessing on social media and in societies themselves. Discourse theory provides researchers with a toolkit—a set of theoretical constructs—to investigate the dynamics involved in the formation of polarised spaces online and offline. Therefore, I use this theory as the theoretical lens of my study. This also allows me to take a step towards introducing the theoretical toolkit of this theory to digital media studies, and to show its value in empirical studies of social media platforms.

Furthermore, another significant factor in favour of using discourse theory in this study is the theory’s strength in addressing two important gaps in the literature.

As I show in Chapter 3, the primary mode of thought with regard to social media and democracy relies on liberal and deliberative models of democracy, which generally aim to achieve democracy through consensus. However, as I later discuss, this mode of thought carries certain limitations and intrinsic paradoxes. Furthermore, with regard to the technological affordances of social media platforms and their role in democratic projects, a large part of our thinking has been influenced by technological determinism, which assumes

Introduction 7 technology as having a direct influence on many of the social phenomena observed on platforms, including polarisation and fragmentation of communicative spaces.

In order to investigate the dynamics involved in the discursive struggles between the demands of users in the Australian Twittersphere, this project pursues three main objectives. The project’s underlying assumption—on which the three objectives are built—is as follows: Users on Twitter build communities through their engagement with the affordances of the platform.

These communities could be polarised and disconnected, or they could be well- connected. In each case, there are particular dynamics involved in the formation of the communicative spaces. These dynamics involve the users and their agency; the material affordances ofthe platform; social and contextual structures; and the discourses in which users operate. Therefore, communities on Twitter are, in fact, discursive-material assemblages, or to use the terminology of network science—'clusters’.

To investigate the dynamics that form clusters, therefore, we need to look into the flow of information and discourse within each cluster; the discursive struggle between antagonistic clusters; and the embeddedness of these clusters and discursive struggles in broader contexts. I call these dynamics ‘intra- cluster’, ‘inter-cluster’, and ‘extra-cluster’ dynamics, respectively. The three objectives of the present research, then, can be formulated as explained below.

What are the intra-cluster dynamics?

To answer the question What are the intra-cluster dynamics? I examine the various ways in which discourses are reproduced within the discursive clusters in the case studies. This involves an examination of the patterns of

8 Introduction communication and information flows by groups of users who share the same discourses. The main focus in this regard, therefore, is to determine what information sources are used and shared; what hashtags are employed; what themes and topics are interdiscursively connected; what words, arguments, discursive strategies, themes, and topics are salient in the discourse of different groups; and how these different groups use the affordances of Twitter to amplify and reproduce their discourses.

What are the inter-cluster dynamics?

Answering the What are the inter-cluster dynamics? question involves a two- step process. First, I need to establish whether the different clusters in the communicative space are exposed to the discourse of the clusters that hold different or opposite views. In other words, I first need to establish whether there is, in fact, any interaction between clusters. If there are no information flows between clusters, asking about the dynamics of flows seems unnecessary.

If there is no interaction, the only thing between the clusters is absence. Of course, absence has its own dynamics also. If the only presence is that of absence, the question to then ask is why this absence occurs. Is it the result of the technological design of the platform, or of user choices? Therefore, an investigation of this question necessitates that I also engage with the questions of filter bubbles and/or echo chambers, and whether there are any spaces that can be conceptualised as such.

The second step in answering this question is to establish the dynamics involved in the manifestation of discursive struggles between the clusters. That is, I need to investigate the discursive struggles between the discourses that are in direct opposition (i.e. enemies), and the discursive struggles between

Introduction 9 allies and/or discourses that are not necessarily oppositional, but different (i.e. adversaries).

What are the extra-cluster dynamics?

The clusters formed on Twitter do not exist in a vacuum. Each cluster is formed as a result of a combination of factors and dimensions, including the technological design and affordances of the platform and its cultures of use, and the socio-political contexts of society. In Section 3.5, I theorise these socio- political contexts as ‘horizontal’ and ‘vertical’. Additionally, the affordances of

Twitter allow for the formation of both short-term and long-term clusters. A cluster formed by a group of users employing a certain hashtag in their tweets, for instance, is more likely to be an ad hoc and temporary formation. Clusters of users following each other, however, are possibly more permanent and long- term.

Therefore, the third objective of this study is to investigate the dynamics involved in the formation of these clusters, and their embeddedness in the contextual particularities of the platform and society. This involves an investigation of, for instance, how different groups of users react to the unfolding of events in society; how they employ the affordances of Twitter to disseminate their discourse; and their argumentation strategies.

An important point to consider with regard to the three objectives described above is that although I present them as three separate objectives, it is not possible to draw distinct lines between them. What I conceptualise as an intra- cluster dynamic, for instance can, at the same time, play its role in a discourse community’s engagement with its antagonist; that is, as an inter-cluster dynamic. Or, to use another example, sharing URLs to different news sources

10 Introduction is simultaneously an intra-cluster and an extra-cluster dynamic; this is because it serves both to amplify one’s discourse and disseminate it to one’s own followers, and to introduce a point of view from outside a cluster on Twitter to the general discussions on the platform.

1.3 Thesis Outline

I structure this thesis based on the analytical constructs and sensitising concepts of the project (Section 2.3.2). Therefore, instead of dedicating a chapter to each case study, I present the three case studies alongside each other to facilitate comparability and the flow of the argument. This allows me to better make comparisons between the three studies when I observe differences, and to avoid repeating the same finding where there are similarities between the cases. The following paragraphs provide an outline of this structure.

Following this introductory chapter, Chapter 2 provides an introduction to the theoretical framework of the study: Laclau and Mouffe’s discourse theory

(Laclau & Mouffe, 2001), and its subsequent development and expansion by

Carpentier (2017). Carpentier theorises the non-hierarchical relationship between the four key elements of my research question: discourse, material, agency, and structure. Since a secondary goal of the present study is to introduce the vocabulary and theoretical framework of discourse theory to the discipline of digital media studies, it is necessary for me to dedicate a section to introducing this theory. Additionally, my interpretations and discussions of the findings of this study draw directly from discourse theory. Chapter 2, therefore, does not directly engage with the object of the study (social media platforms or Twitter). Rather, it sets the stage for the whole project, and introduces the key terms and definitionsthat are used throughout the project to facilitate the interpretation of its findings. At the same time, Chapter 2

Introduction 11 serves as a basis for a re-reading of the social media literature from a new perspective, as explored in Chapter 3.

Chapter 3, which aims to review the literature, is structured in an inverted pyramid fashion. I start with broader theories on the inter-relationship between social media and democracy; move to mid-level and empirical studies of social media and the public sphere; and, finally, focus on the objects of the study in this project—Twitter in general, and the Australian Twittersphere in particular. Given the interdisciplinary nature of the study, this chapter also engages with studies that focus on the materialities of platforms, such as algorithms, design, and affordances. I follow the development of concepts and theories that explain social media and their relationship to the public sphere, and show how the primary model of democratic thought with regard to social media and democracy has traditionally privileged liberal and deliberative models of democracy. Reviewing the literature from the perspective of discourse theory, I move to argue for an alternative approach to our understanding of democracy, namely, that of agonistic pluralism.

The fourth chapter in the thesis provides details of the methodological design of the project, and the different phasesof the study. In this chapter, I provide the rationale for my choice of tools and methods, and show how these come together under the lens of discourse theory. In the methodological design of this project, a key goal for me was to design the project in a way that can account for both the large datasets and broad communicative patterns, and for the deeper insights gained from discourse-theoretical analysis. To achieve this goal, the methodological process follows an iterative move from quantitative and large-scale social media analytics to a qualitative close reading of tweets.

12 Introduction Chapter 5 introduces the three case studies, and discusses the context, background, and history of each. This chapter builds the contextual foundation for the interpretation of the findings of each case study in the subsequent chapters, and introduces these Australian-specific topics to non-Australian readers. The next three chapters present the findings of, and initial discussions arising from the three methodological phases of the study.

In Chapter 6, I focus on the temporal, collective, and aggregate patterns of activity in each case study, and show how the different users, communities, and clusters in each case make strategic discursive choices to reproduce their discourse, amplify their voice, and gain discursive power.

In Chapter 7, I shift my attention to the dynamics involved in the formation of online discursive clusters, and use social network analysis to investigate the flow of information within and between these clusters. This chapter introduces the concepts of networked discursive alliances and active passivity—two of the key findings of this project.

Chapter 8 moves away from the dynamics of networks and affordances, and focuses primarily on the textual representation of discourses. This chapter shows how different discourses in a networked discursive alliance maintain their identity while forming an alliance with the hegemonic discourses. It further develops the concept of active passivity by showing how passivity becomes a discursive strategy; that is, users avoid engaging with topics and communities that they find discursively dissonant. In light of the findings of its previous chapters, Chapter 8 shows that the observed passivity is not because users are not exposed to the discourse of the Other, but because they actively avoid engaging with it.

Introduction 13 Chapter 9 synthesises the findings of Chapters 6, 7 and 8, and primarily focuses on the discussion of three main issues. First, through reviewing the findings of these previous three chapters, I develop and expand the concept of networked discursive alliances, and show how the affordances of Twitter are strategically discursified by its users to build temporary spaces in which antagonistic discourses engage. Furthermore, I turn my attention to the second key finding of the study: the dynamicsinvolved in, and the democratic implications of, the active passivity of Twitter users. Finally, I use these two findings to also engage with recent debates about filter bubbles and echo chambers. I reflect on discourse theory and the empirical findings of this research to investigate whether filter bubbles and echo chambers exist, and if they do, on the factors that might contribute to their presence.

Finally, Chapter 10 concludes this study by summing up its main findings, setting the stage for future work, and engaging with the key question of the study: the conditions in which social media can play a positive role in a democratic project.

14 Introduction 2 Theoretical Framework

Theoretically, this study draws from the discourse theory of Laclau and Mouffe, originally developed in Hegemony and Socialist Strategy (Laclau & Mouffe,

1985). Following the publication of this seminal work, their subsequent works, either collaboratively or individually, further deepen and develop their discourse theory.

As pointed out in the introductory chapter, not many studies approach digital media studies from a discourse theoretical perspective. Although media and communication studies and discourse theory have many common interests and vantage points, disciplinary differences between them mean that an expert in one of these disciplines might use different terminology than one in another.

Additionally, and especially regarding the particular theoretical vocabulary used in each discipline, a lack of proper introduction could lead to confusion and misunderstanding between the two fields.

This risk of confusion and misunderstanding requires me to dedicate a chapter of this thesis to the introduction of the theoretical foundation of the study.

Therefore, while a large part of this chapter does not directly engage with the object of this study (i.e. social media in general, or Twitter specifically), my aim here is to establish the premises that I use to re-read and review the social media literature in the following chapter. I begin this chapter with an introduction to discourse theory and its constructs, and then move on to show how this theory can be operationalised in a study of social media platforms.

Theoretical Framework 15 2.1 Discourse Theory

The development of discourse theory is the result of a deconstruction and combination of two bodies of work dominating the Social Sciences in the 20th century: Marxism, and structuralism (Jørgensen & Phillips, 2002, p. 25).

However, Laclau and Mouffe’s deconstructionist approach, in-depth theoretical engagements with a range of theories, and writing style, mean that their bodies of work are sometimes considered as quite hermetic and impenetrable

(Carpentier & De Cleen, 2007).

This characteristic is further complicated by the multi-layered nature of their work. At one level, Laclau and Mouffe present a social ontology (Howarth,

2000) that mainly deals with their engagement with what they conceive to be shortcomings in structuralist thought. At this level, they also engage with the reductionism in Marxist thought, which they view as an essentialist reduction of all social phenomena to economic and social classes. At this ontological level,

Laclau and Mouffe’s post-structuralist, post-Marxist theory presents itself as an ontology of the social that is radically materialist, yet places an emphasis on discourse as the source of meaning-making. In this sense, unlike Foucault, who distinguishes between the discursive and non-discursive, Laclau and

Mouffe view discourse as the mediator through which the material gains meaning (Laclau & Mouffe, 1985, p. 107).

At the second level of discourse theory, which is built on the theoretical premises of the first level, Laclau andMouffe mainly focus on a theory of

‘political identity’ (Smith, 1999, p. 87, as cited by Carpentier & De Cleen,

2007), and on the way discourses are in a contingent struggle to attain fixity of meaning and become hegemonic. The concepts of antagonism and contingency can be seen as the key concepts at this level.

16 Theoretical Framework Finally, at a third level, Laclau and Mouffe’s theory presents itself as a political theory. Building on the two other levels, they call for a radical pluralistic democratic politics that engages mainly with the works of other theorists of democracy, such as Habermas, Schmidt, and Arendt. In the project at hand, the first two levels of discourse theory act as the guiding principles, constructing the lens through which I investigate the dynamics of discursive struggle in the Australian Twittersphere. Although I engage with the political theory of Laclau and Mouffe throughout the thesis, my main focus is on the first two levels.

Laclau and Mouffe introduce a set of complex and inter-dependent concepts in their theorisation of discourse. To introduce their theory and the theoretical framework of this project, I begin with some of their key theoretical concepts, as defined inHegemony and Socialist Strategy:

[W]e will call articulation any practice establishing a relation among elements such that their identity is modified as a result of the articulatory practice. The structured totality resulting from the articulatory practice, we will call discourse. The differential positions, insofar as they appear articulated within a discourse, we will call moments. By contrast, we will call element any difference that is not discursively articulated. (Laclau & Mouffe, 1985, p. 105, emphases in the original)

I then expand each of these definitions with more detail, and move on to the introduction of other theoretical constructs arising from these key concepts.

2.1.1 Discourse

As can be seen from the quote above, the definitions of the four concepts used in the discourse theory are highly interdependent. This is in line with the general post-structuralist approach of Laclau and Mouffe, which views discourses as structured totalities, within which moments find their meanings

Theoretical Framework 17 only through differential positions. That is, no moment has an intrinsic, objective meaning that can be defined in isolation. However, to illuminate the definitions of the terms, I firstconsider each separately.

Laclau and Mouffe’s concept of ‘structured totality’ (in the definition above), understands discourse to be comprised of “all the practices and meanings shaping a particular community of social actors” (Howarth, 2000, p. 5). In such an understanding, every social phenomenon, object, practice, event, and so on, obtains its meaning from discourse. A discourse, therefore, is a network or a web of connected, inter-dependent, inter-related signifiers. Jørgensen and

Phillips use the metaphor of a fishing net and its knots to show this web of signifiers (2002, p. 25).

For Laclau and Mouffe, discourse, therefore, is not only a linguistic construct.

Many of the other traditions and approaches that come together under the umbrella term ‘discourse analysis’, in one way or another rely on a linguistic aspect of discourse. In contrast, Laclau and Mouffe’s discourse theory expands the definition of ‘discourse’ to cover everything with a meaning, or everything to which meaning can be ascribed; this includes the linguistic, the social, and the physical. This understanding of discourse is one of the key differences between discourse theory and the Foucauldian notion of discourse, which distinguishes between discursive and non-discursive practices, or between discourse theory and critical discourse studies. This is particularly true of

Fairclough’s framework, which mainly concerns itself with the linguistic aspect of discourse (See Fairclough, 2013a, 2013b).

Returning to Jørgensen and Phillips’ (2002) fishing net metaphor, discourse, therefore, is a web of signifiers. However, Laclau and Mouffe introduce another important concept in their formulation of discourse. Just as a fishing net relies

18 Theoretical Framework on a first knot to which all subsequent knots are tied and connected, for Laclau and Mouffe, discourse also relies on “privileged discursive points” (Laclau &

Mouffe, 1985, p. 112)—central signifiersthat bring together all other moments in the discourse. Laclau and Mouffe (ibid.) refer to these central, privileged signifiers as ‘nodal points’. Any sign in the network of a discourse acquires its meaning from its differential relation to the nodal point. Jørgensen and Phillips use the example of ‘body’ as the nodal point of the medical discourse, in that all the other signs in the discourse acquire their meaning from their differential relation to the body. The meaning of signifiers such as ‘scalpel’ or ‘doctor’ relies on their relation to the body (Jørgensen & Phillips, 2002, p. 26).

This brings me to another key concept in discourse theory. The point of divergence between discourse theory and structuralist models of language and society before it emerges in Laclau and Mouffe’s emphasis on the point that no structure is completely fixed and permanent. Rather, since signifiers always acquire their meaning from their differential positions in relation to each other, the nodal points of a discourse are also empty of a fixed meaning. Furthermore, a nodal point is also not exclusive to one discourse only. To use Jørgensen and

Phillips’s example again, ‘body’ could be the nodal point of Western medical discourse, religion, and alternative medicine at the same time, with each discourse ascribing different meanings to the word. In this sense, although a signifier can be the nodal point of several discourses, it never has a fully fixed meaning. This understanding becomes the basis for the definition of ‘floating signifiers’ in discourse theory. However,the concept of floating signifiers is not only limited to the nodal points of a discourse; rather, any signifier that is so

“overflowed with meaning” and is ascribed different meanings in different discourses, is a floating signifier (Torfing, 1999, p. 301).

Theoretical Framework 19 The formulation of floating signifiers adsle to a number of other key concepts in discourse theory. As discussed above, signifiers can be ascribed different meanings by different discourses, in differential relations to the other signifiers in that discourse; therefore, they constantly slide between different discourses.

This leads to some signifiers being so “over-coded” (Torfing, 1999, p. 301) in different discourses that no particular signified can be conceptualised for them.

These “signifier[s] without signified[s]”are called ‘empty signifiers’ (Laclau,

2007, p. 36). Torfing uses the signifier emocracy’‘d to exemplify this. While the notion of democracy is the nodal point of several discourses, the term does not have any particular meanings per se. Rather, “it means everything and nothing” (Torfing, 1999, p. 301).

Given that the signifiers in a discourse never have a fixed meaning, and are always prone to being ascribed new meanings by other discourses, Laclau and

Mouffe introduce the next two conceptsin their discourse theory: contingency and closure. I have so far established that within a discourse, there is always a differential relationship between the signifiers, which themselves have formed around a nodal point. However, the totality created by these relations— discourse—is never fully fixed and stable: It is always threatened by competing discourses that attempt to hegemonise their own meanings. In this sense, every discourse is contingent, and never has a fixed meaning.

The contingency and lack of closure of discourses, however, should not be understood as indicating that there is no order and structure to a discourse.

As Laclau and Mouffe argue, “a discourseincapable of generating any fixity of meaning is the discourse of the psychotic” (Laclau & Mouffe, 1985, p. 112).

Any discourse, therefore, always has a partially fixed structure, and attempts to achieve the final permanent stabilityand fixation, which Laclau and Mouffe

20 Theoretical Framework call ‘closure’. This very inclination of discourses to achieve full fixity of meaning is what creates discursive struggles. In the struggle to attain fully fixed meanings and achieve closure, some discourses are more successful, and

(partially) fix their structure so that they become naturalised for the social actors and become common sense—an ‘objective’ reality taken for granted by all the social actors using them. Upon achieving this hegemonic position, a discourse is said to be ‘sedimented’:

Insofar as an act of institution has been successful, a ‘forgetting of the origins’ tends to occur; the system of possible alternatives tends to vanish and the traces of the original contingency to fade. In this way, the instituted tends to assume the form of a mere objective presence. This is the moment of sedimentation. (Laclau, 1990, p. 34)

This condition is the basis of what Laclau and Mouffe call ‘hegemony’. A hegemonic discourse, therefore, is a discourse that has succeeded in attaining a level of fixity in its meaning structure so that it is less likely to be disturbed by other discourses. The political struggle between competing discourses to become hegemonic in a society is called a ‘hegemonic project’, whose objective is “to construct and stabilise nodal points that are the basis of a social order”

(Carpentier, 2017, p. 21).

2.1.2 Elements, Moments, Articulation

With the broader picture of discourse and discursive struggles in mind, we can now pay closer attention to the notions of articulation, moments, and elements.

A discourse, as conceptualised above, is a network of signifiers, each finding its meaning through its differential relation with the others, and especially through the central, privileged signifier called the ‘nodal point’. In Laclau and Mouffe’s discourse theory, these signifiers are called ‘moments’, rather than signifiers, mainly because the notion of signifier necessarily encompasses only linguistic

Theoretical Framework 21 constructs. However, as already discussed, discourse theory accounts for linguistic, social, and physical entities together. A moment, therefore, is a signifier that has entered the complex network of a discourse. Outside the network of a discourse, however, one can imagine a multitude of other signifiers, which are referred to as ‘elements’. The process of turning elements into moments, hence bringing them into the web of a particular discourse, is what Laclau and Mouffe refer to as ‘articulation’.

It is worth noting that the distinction between moments and elements is necessarily a relational one. Within the boundaries of a certain discourse, all the connected relational entities are moments. However, each of these moments is always threatened by the possibility of being articulated by a competing discourse. In this sense, what is a moment for a certain discourse, is an element for another.

The possibility of being articulated by another discourse lies in the fact that any articulation is the ascription of certain meanings to an element, hence transforming it into a moment. Therefore, any meaning-ascription comes at the expense of excluding all alternative meanings. This becomes the basis of the formulation of the notions of ‘antagonism’ and ‘contingency’ in discourse theory. Because every articulation is exclusionary, effectively repressing alternative meanings, any meaning making process—discourse—is effectively political:

[E]very order is political and based on some form of exclusion. There are always possibilities that have been repressed and that can be reactivated. The articulatory practices through which a certain order is established and the meaning of social institutions is fixed are ‘hegemonic practices’. Every hegemonic order is susceptible of being challenged by counter-hegemonic practices, i.e. practices

22 Theoretical Framework which will attempt to disarticulate the existing order so as to install another form of hegemony. (Mouffe, 2005, p. 18)

2.1.3 Identity and Antagonism

The articulation of elements into discourses, therefore, is what constructs their identity, be it the identity of “objects, individuals or collective agents”

(Carpentier & De Cleen, 2007, p. 267). Given the contingency of discourses, the identity of moments in a discourse is also contingent, since there is always a multitude of competing discourses into which an element can be articulated.

Laclau and Mouffe call this space outside a discourse—where all the competing meanings, discourses, and elements are located—the ‘field of discursivity’. The field of discursivity, therefore, is a contested space that discourses attempt to dominate (Laclau & Mouffe, 1985, p. 112).

This brings us to the second level in discourse theory, where we find the concepts of antagonism and hegemony. For Laclau and Mouffe, two extreme cases of discursive structures can be envisaged. On the one side, there can be a discourse in which no meaning is stable and fixed. For Laclau and Mouffe

(1985, p. 112), such a discourse can only be understood as the “discourse of the psychotic”, since it makes it impossible for any meaning to be formed. On the other side, one can imagine a discourse with a fully fixed meaning structure; that is, a fully sedimented discourse. In such a formation, the only possibility is repetition, and no change or social progress can take place (Laclau & Mouffe,

1985, p. 134). Both these extremes, therefore, are theoretical and practical impossibilities since the lack of full fixity of meaning and closure is the very necessity of social change.

This is the foundation of the concept of antagonism as theorised in discourse theory. In their struggle for hegemony, discourses operate in frontiers “criss-

Theoretical Framework 23 crossed by antagonisms” (Laclau & Mouffe, 1985, p. 135). These antagonisms always have a dualistic nature, with both positive and negative aspects. At an antagonistic frontier, a discourse attempts to destabilise the identity of the

Other, domesticate its moments, and become hegemonic. At the same time, a discourse is radically reliant on this very Other to stabilise its own identity, since all meaning is constructed in a radically negative way as a result of differential relations. Total elimination of the Other, therefore, deprives a discourse of its very conditions of possibility, since it eliminates its

“constitutive outside” (Howarth, 2000, p. 123).

2.1.4 From Antagonism to Agonism: Chains of Equivalence

An important consideration is that antagonism per se should not be understood as merely an oppositional relationship between two discourses only, with the purpose of the total elimination of the Other (e.g. Left vs. Right; Friend vs.

Enemy). One can conceptualise a multitude of antagonisms. In such a relationship, an antagonistic frontier in a society can be seen as being constituted by two (or more) main sides, each containing a series of discourses coming together to destabilise and eliminate the Other side. Such a condition brings us to another key concept in discourse theory, which Laclau and Mouffe theorise as ‘logics of equivalence’.

At an antagonistic frontier, discourses can form alliances against the enemy.

One can imagine a political situation where discourses come together against a common enemy. In a revolution against a dictatorship, for instance, different discourses can form an alliance. These could be, for example, the discourses of republicans, democrats, communists, or anarchists, all coming together against

24 Theoretical Framework the dictator1. In such a condition, two different sets of antagonisms can be observed. On the one side of the antagonistic frontier, there is the range of discourses that form an alliance. Although each of these discourses still attempts to maintain its fixity and closure, their identity is modified in a way that puts them all together in a chain of equivalence against the enemy. In

Laclau and Mouffe’s words:

Social actors occupy differential positions within the discourses that constitute the social fabric. In that sense they are all, strictly speaking, particularities. On the other hand, there are social antagonisms creating internal frontiers within society. Vis-à-vis oppressive forces, for instance, a set of particularities establish relations of equivalence between themselves. It becomes necessary, however, to represent the totality of the chain, beyond the mere differential particularisms of the equivalential links. (Laclau & Mouffe, 2001, p. xiii)

In this sense, there are two antagonisms present simultaneously. One is between the allies and their common enemy, where each ally has transformed itself “in the universality transcending it”. However, it has done so “without ceasing to be its own particularity” (ibid.). Therefore, we can also conceive of another level of antagonism among each of the discourses in the chain of equivalence. This is the basis of the agonistic pluralism that is the political emancipatory goal of Laclau and Mouffe’s discourse theory.

Within a chain of equivalence, therefore, antagonisms among some discourses are temporarily transformed in a hegemonic project. In this chain, the discourses in the equivalential chain are no longer enemies that should be eliminated. Rather, they are adversaries, acknowledging their differences, and the fact that these differences might neverbe resolved. Nevertheless, they come

1 This very condition happened in my country, Iran, during the 1979 revolution, where a range of discourses formed an alliance in the revolution against the Pahlavi regime.

Theoretical Framework 25 together to achieve a greater, common purpose: the elimination of their common enemy. Given the important difference between these two types of antagonism, I use the terms ‘adversarial antagonism’ and ‘inimical antagonism’ throughout this thesis to emphasise which form of antagonism I am referring to. As Mouffe points out, what sheperceives as agonism is essentially a particular form of antagonism, which “is not eliminated, but ‘sublimated’”

(Mouffe, 2013, Chapter 1).Following the same logic, I use ‘adversarial antagonism’ to refer to the discourses in an equivalential chain that have come together against an Other enemy. In contrast, I use ‘inimical antagonism’ to refer to the discourses on the two sides of an antagonistic frontier, where the purpose of each is to eliminate the Other.

Howarth (2000) uses a mathematical formulation to conceptualise chains of equivalence. Assigning letters to discourses, Howarth formulates a chain of equivalence between discourses ‘a’, ‘b’, and ‘c’, against an antagonist discourse

‘d’. Therefore, the chain of equivalence can be shown as a = b = c, while they all come together against ‘d’, in d = –(a,b,c) (Howarth, 2000, p. 107). In this formulation, therefore, there is an adversarial antagonism between ‘a’, ‘b’, and

‘c’, and an inimical antagonism between the three and their common enemy, the discourse of ‘d’. The adversarial antagonism in the formulation is referred to as ‘agonism’ by Mouffe (2013), whoargues that the goal of a democratic project should be to transform inimical antagonism between Other-enemies into adversarial antagonisms. In her own words: “What is important is that conflict does not take the form of an‘antagonism’ (struggle between enemies) but the form of an ‘agonism’ (struggle between adversaries)” (Mouffe, 2013,

Chapter 1). “[T]he aim of democratic politics is to transform antagonism into agonism” (Mouffe, 1999, p. 16;emphasis in original).

26 Theoretical Framework 2.2 The Discursive-Material Knot

One of the criticisms directed at discourse theory is the claim that it is an idealist theory. However, an oft-cited section of Laclau and Mouffe’s book is used as a starting point to refute such criticism:

An earthquake or the falling of a brick is an event that certainly exists, in the sense that it occurs here and now, independently of my will. But whether their specificity as objects is constructed in terms of ‘natural phenomena’ or ‘expressions of the wrath of God’ depends upon the structuring of a discursive field. What is denied is not that such objects exist externally to thought, but the rather different assertions that they could constitute themselves as objects outside any discursive condition of emergence. (Laclau & Mouffe, 2001, p. 108)

In another work, Laclau and Mouffe argue for the same position by distinguishing between existence and being of the material, by acknowledging that the material, in its pure materiality, exists, regardless of our will or perception. However, it only comes into being once we articulate it in a certain discourse.

[O]utside of any discursive context objects do not have being; they have only existence. […] things only have being within a certain discursive configuration, or ‘language game’, as Wittgenstein would call it. It would be absurd, of course, to ask oneself today if ‘being a projectile’ is part of the true being of the stone. […] the answer, obviously, would be: it depends on the way we use stones. (Laclau & Mouffe, 1990, pp. 104–105, emphases in original)

However, although—as different scholars show (see, for instance, Howarth,

2000; and Torfing, 1999)—Laclau and Mouffe’s theory does account for, and places a significant weight on the material, their general emphasis and focus on the discursive in their analyses is still an issue. As Carpentier argues, some material components of reality—such as objects, technologies, or human

Theoretical Framework 27 interactions—remain quite undertheorised in the original theorisation of discourse theory (Carpentier, 2017, p. 37).

Having established the important role of the material in discourse theory, the question which remains, then, is how to treat the discursive and the material in a way that does not privilege one over the other. Furthermore, returning to the social ontology of Laclau and Mouffe, which distances itself from the dichotomy between idealism and materialism, another dimension presents itself between the role of agency and structure. The question in this regard is how to maintain a post-structuralist ontology and avoid structuralism—which considers fixed structures for society, and does not allow for contingency— while at the same time accounting for the role of agency in the processes that lead to social change.

It is precisely here that Carpentier’s conceptualisation of the discursive- material knot (Carpentier, 2017) finds its significant role in this current research. Given that the object of study is a social media platform with its own internal logics, technological affordances, algorithms, and other characteristics, the role of the material is ineradicable from this project. Concurrently, a platform is a collection of users with their own agency and discourses, who interact and operate on the platform using its technological material affordances. Additionally, the platform itself, and the users on it, are all situated in broader socio-political contexts that have their own impact on the structures, discourses, and agency of individuals on, and beyond the platform.

Therefore, any social interaction on the platform is a combination of discourse, agency, structure, and material.

Especially in the context of social media platforms, one cannot ignore any of these four components without falling to essentialist and reductionist

28 Theoretical Framework conclusions. An over-focus on the agency of users, for instance, ignores the technological affordances that allow for the actions of the individuals and shape their behaviour on the platform. Conversely, an over-focus on technological affordances pushes the conclusionsof a study towards technological determinism, and treats the materiality of a platform as an independent agency that defines and dictates actions on the platform. The same problematic is true for an over-focus on the discursive that potentially ignores the role of the material affordances of the platform inshaping discursive formations. Finally, an over-emphasis on structure, without accounting for the other three aspects, eventually leads to essentialist and reductionist understandings that do not have any room for social change.

The discursive-material knot, therefore, finds its significance as both a theoretical approach and an analytical framework in this study. Carpentier calls for a non-hierarchical understanding of the four aspects discussed above, in a knotted fashion that cannot, and should not be untied (Carpentier, 2017, p. 14). Throughout this study, I investigate the four strands of the knot— sometimes together, and sometimes separately for more clarity. However, more emphasis is at times put on the discursive and the material aspects, and not as much on the agency and structure. This is because the primary focus of the project is to investigate how discursive struggles occur on a social media platform, especially with regard to its technological materialities.

2.2.1 The Four Strands of a Knot

As the diagrammatic overview of the discursive-material knot shows, its four components are intertwined and inseparable. In this section, I provide a (very)

Theoretical Framework 29 brief summary of their inter-relationships as Carpentier has conceptualised them2, and then move to the sensitising concepts of this project.

Figure 2-1: ‘Overview’ of the discursive-material knot (Carpentier, 2017, p. 67) Starting from the agency component and its relationship to the other three components, Carpentier emphasises the role of “identificatory choices”. Faced with a plurality of competing discourses, an individual’s agency comes into play in their identification with particular discourses, and in the particular ways in which they identify with these discourses. As parts of the social, individuals “collectively construct discourses through their signifying practices, and become part of the many discursive struggles that define that very social”

(Carpentier, 2017, p. 68). At the same time, “discursive structures themselves are constructed through the workings of agencies” (ibid.). Finally, the same

“constructive capabilities of agency apply to material structures” (ibid.).

2 A fully detailed account of this complex theoretical work is not possible here due to the word limits of a doctoral thesis. While I have tried to accurately present this summary here, I admit that I cannot do justice to the original text.

30 Theoretical Framework The role of the material in its inter-relationship with the three other components is, first, in the way the material “constructs the social through its very materiality” (ibid., p. 69). As Carpentier argues, “objects […] through their affordances, allow for particular actions to be performed, and dissuade the performance of others” (ibid., p. 70). This inter-relationship of the material and the structure opens two possibilities. Through its affordances, the material privileges certain actions over others. In other words, although an object can be articulated in numerous ways, its material affordances call for certain actions as preferential. One might be able to, for instance, eat soup with a knife—however difficult it may be—but the affordances of the knife privilege actions such as cutting, slicing, or chopping over eating soup. Carpentier (2017,

2019) conceptualises this constructing feature of the material as ‘invitation’:

[M]aterials extend an invitation to be discursified, or to be integrated in discourse, in always particular ways. These invitations, originating from the material, do not fix or determine meanings, but their material characteristics still privilege and facilitate the attribution of particular meanings through the invitation. (Carpentier, 2017, p. 45)

On the other hand, through its affordances, the material also simultaneously dissuades other actions. In this sense, the affordances of the material can de- structure and disrupt the society and human actions. Finally, through the meanings ascribed to the material by individuals and collectives, the material

“can also be used as the building blocks of human agency” (Carpentier, 2017, p. 71).

This brings us to the role of the discursive, in that the agency of the individual itself is always embedded in their identification with certain discourses among a plurality of alternative discourses. In this sense, the way an individual gives meaning to a material through their agency is always embedded in the

Theoretical Framework 31 discourses they identify with. Returning to Laclau and Mouffe’s example of a stone: The eventual answer to whether a stone is a projectile or not depends on its discursive context.

Finally, the structure component finds its relationship to the three others when we consider the “assemblages of materials” into “structured entities”

(Carpentier, 2017, p. 73). These assemblages could be understood as the way different materials are combined to make a certain object, or as the way different objects are arranged in an ensemble to create a space. Such a space, by its materiality per se, constructs human behaviour through privileging certain acts and dissuading others. In this sense, material assemblages are constituted and constrained by structures while, at the same time, they constitute and construct agency of individuals; this, in turn, impacts the structure.

2.3 Operationalising Discourse Theory

One of the frequently seen criticisms of discourse theory, and perhaps one that has had an important role in a sensed reluctance to incorporate this theory in media studies, is that Laclau and Mouffe actively refuse to provide a methodological guideline for their theory (Dahlgren, 2011, p. 226). This refusal is, of course, completely in line with their post-structuralist approach, which necessarily avoids essentialism, and instead opts for open-ended frameworks

(Carpentier & De Cleen, 2007).

To move from a level of high theory3—which, as discussed, does not have clear methodological guidelines—to a level that can be operationalised in an

3 This is a term that I have frequently seen used in reference to theories that mainly deal with ontological and epistemological questions, without necessarily aiming to theorise a particular

32 Theoretical Framework empirical research project, Carpentier relies on, and develops, the concept of

‘sensitising concepts’ in various works (see, for instance, Carpentier, 2010,

2017; Carpentier & De Cleen, 2007). Sensitising concepts are concepts that show a researcher “what to look for and where to look” (Ritzer, 1992, p. 365, as quoted by Carpentier, 2010). In a research study that is epistemologically and ontologically reliant on discourse theory, therefore, the primary sensitising concept is the notion of discourse itself. Under this broad primary sensitising concept, one can also find the secondary sensitising concepts drawn from discourse-theoretical analysis, such as articulation, nodal points, and hegemony

(Carpentier, 2017, p. 78). Finally, tertiary sensitising concepts arise from the particular object of research in each study, and consist of concepts, notions, and questions that the research project aims to investigate and answer. These, of course, are always (re-)articulated and (re-)interpreted through a discourse- theoretical lens, building on the primary and secondary sensitising concepts

(Carpentier, 2017, pp. 292–296).

2.3.1 Sensitising Concepts

To move from high theory to empirical research, Carpentier (2017) suggests a return to the basic principles of qualitative research, and the use of the sensitising concepts introduced by Blumer (1986) as the guiding concepts that help the researchers to decide which aspects of the study they should focus on.

Following the general approach of post-structuralism, Carpentier also calls for research-specific constructionsof sensitising concepts.

social phenomenon. The aim of such work, as is the case for Laclau and Mouffe’s oeuvre, is theory building. Reisigl and Wodak (2016) use the term ‘grand theories’ to refer to the same thing.

Theoretical Framework 33 Using the model of a pyramid for the construction of the different levels of analysis and sensitising concepts for a project, Carpentier shows how the top of the pyramid is occupied by the primary sensitising concepts, which are the nodal points of the theory and inform the whole research project (in this project, discursive-material analysis). The different levels below this, therefore, are the sensitising concepts that stem from the various aspects and focal points of the study. For a study that is interested in discourse and materiality, for instance, the secondary sensitising concepts are those derived from theories of discourse and materialist theories. Finally, levels below the secondary sensitising concepts, such as tertiary (or, if needed, more) concepts, are those built on the broader premises of qualitative/quantitative research methodologies. At each level, therefore, it is the sensitising concepts that guide the study. However, each project needs to rely on the different tools, methods, and techniques that are dictated by its questions and specificities.

Figure 2-2: The primary and secondary sensitising concepts of discursive-material analysis (Carpentier, 2017, p. 293). (DT= discourse theory; MT= materialist theories; DMA = discursive-material analysis)

34 Theoretical Framework 2.3.2 Sensitising Concepts of the Project

The present project is particularly interested in the dynamics of discursive struggle in the Australian Twittersphere. Returning to the dictionary definition of the word ‘dynamics’, therefore, I am more interested in analysing the properties or forces that drive actions on the platform. These forces, seen from the perspective of discourse theory—and, particularly, discursive-material analysis—are comprised of both the material aspects of the platform and its technological affordances; and of the individuals doing the actions on the platform, inscribing their own discourses into the materialities, and discursifying them. Therefore, at the most empirically-oriented, data-driven level of this study lie the range of tools, methods, and techniques used for the analysis of behaviours and information flows on social media platforms; namely, social media analytics and social network analysis. However, the mere quantification of actions on the platform does not alone provide the answers to the questions of the study. The findings produced by such metrics need to be interpreted by a human researcher.

At the level of interpretation, therefore, this research builds on the secondary sensitising concepts that are internal to discourse-theoretical and discursive- material analysis. At this level, I investigate the dynamics of discursive struggles in the formation of clusters in the networks, and the role of the different dimensions of the discursive-material knot in such formations. It is at this level that I turn my attention to the role of the affordances of Twitter and the discursive strategies employed by communities of users, to both reproduce their discourses and discursify the affordances of the platform. Of course, these two levels—the empirically-oriented, data-driven level and the interpretation level—are themselves investigated under the epistemological and ontological

Theoretical Framework 35 premises of discourse theory, and the primary sensitising concepts to which every other signifier in this theory is connected. As shown in previous sections, these are concepts such as the discursive, the material, and the discursive- material knot itself.

Following the model proposed by Carpentier (2017, p. 296), Table 2-1 provides a summative account of the sensitising concepts of this study. At the level of primary and secondary sensitising concepts, therefore, there are many similarities between this study and any other discursive-material investigation.

The tertiary sensitising concepts, however, are more directly related to the object of study in each project, and more points of divergence between the present research and other discourse-theoretical studies can be noticed at this level.

Table 2-1: Sensitising concepts of the study

Level Definition Usages (in this thesis) Primary Theoretical nodal points The discursive, the material, the sensitising internal to the methodology discursive-material knot concepts of discourse-material analysis (DMA) Secondary Theoretical elements internal Articulation, nodal point, hegemony, sensitising to the DMA-methodology antagonism, agonism, assemblage concepts (cluster), invitation, etc. Tertiary Theoretical nodal points Social media platforms, vertical and sensitising external to the DMA- horizontal contexts, affordances, public concepts methodology sphere, fragmentation, interaction, visibility, platform logics, etc. Theoretical elements external Networks, homophily, polarisation, to the DMA-methodology alliances, online discourse communities, adversary, enemy, active passivity, crowd-sourced elites, etc. 2.4 Setting the Limits

Apart from consideration of the sensitising concepts introduced in Table 2-1, I needed to set specific boundaries to prevent this research turning into an open- ended, never-ending project. Given that this project is a case-based study, and

36 Theoretical Framework especially given its focus on the four aspects of the discursive-material knot, it is practically impossible to fully follow the strands in each dimension of the knot. Therefore, I have set boundaries, and acknowledge the limitations of time, space, and resources.

I have set these limits based on the horizontal and vertical contexts of the studies (see Section 3.5). Regarding the material, for instance, I focus mainly on the affordances of the platform. However, the materialities of Twitter are not simply confined to its affordances. Rather, they span far beyond the platform itself to the devices used by individuals to access the platform; to the technologies enabling access; to the technologies enabling content distribution at the scales observed on Twitter; to the Internet itself; and to electricity–to name but some of the technological materialities.

With regard to the discursive, I have limited the discourses to those in the

Australian socio-political sphere. However, these discourses themselves are intertwined with global discourses; discourses of, in, and about social media platforms; discourses shaping the decisions of platform owners and moderators; discourses of those writing the code for the algorithms used on the platform; and with innumerable other related discourses. Therefore, this project is far from a comprehensive research study that can answer all questions regarding the inter-relationship of the discursive and the material on social media platforms, or the inter-relationship of social media and democracy. Rather, it is simply a first step in introducing the theoretical vocabulary of discursive- material analysis to digital media studies and, in particular, to platform studies.

In the discussions up to this point, I have presented the theoretical framework of the study, engaging mostly with the primary and secondary sensitising

Theoretical Framework 37 concepts of the research. Given the focus of the present project on social media platforms in general, and on Twitter specifically, in the following chapter I review the literature around the tertiary sensitising concepts of this study from a discourse-theoretical and discursive-material lens. I present this literature review in an inverted triangle: I start from the broader theories and views about social media and democracy, and move towards more empirical and fine- grained literature that investigates different aspects of social media communications.

38 Theoretical Framework 3 Literature Review

All those people down there believed the [Berlin] wall was dividing the world; that East and West were separating people into ‘us’ or ‘them’, but the real division, the only one that has ever mattered, was never horizontal. It’s vertical. (Wachowskis, 2016)

3.1 Introduction

In the previous chapter, I introduced the theoretical framework of the study, and the sensitising concepts of this project. The goal of Chapter 2 was to engage with the primary and secondary sensitising concepts of the study. In the present chapter, I move to the project’s tertiary sensitising concepts.

A number of intertwined concepts guide this research endeavour and its overarching question (see Section 1.2). In the previous chapter, I engaged with notions of discourse, antagonism, discursive struggles, and the discursive- material knot. In this chapter, I turn my attention to the notions of democracy; the materialities of platforms; context and structure; and the particularities of the object of the study: Twitter and the Australian Twittersphere.

Overall, I move from broader and more general constructs and notions to more specific and fine-grained studies in thischapter. Therefore, I start with views and theories that engage with the inter-relationship of social media and democracy. In the first half of the chapter, I follow the range of studies that investigate this inter-relationship, and re-read them from a discourse- theoretical perspective. I then focus on the range of views about the materialities of platforms, and the role they play in the flow of information and democratic projects. Finally, this chapter focuses on the specific object of the study—Twitter and, particularly, the Australian Twittersphere.

Literature Review 39 3.2 Social Media and Democracy

One of the key differences marking the move from traditional mass media to the so-called ‘new media’ and (especially) the new digital media, was the shift from the one-to-many forms of information flows to the new paradigm of many- to-many flows enabled by the Internet and (particularly) the participatory web, often referred to as ‘Web 2.0’. Naturally, this shift brought about a range of views regarding the implications of this new mode of communication and interaction for democratic projects.

In defending the potentials of the Internet, and especially the participatory web, scholars such as Castells (2010) emphasise the democratic potentials of the Internet, and the shifts in power structures created by individuals’ ability to self-communicate, network, and have more autonomy. Others pay more attention to the issue of participation. Jenkins (2008), Shirky (2011), and

Benkler (2006), for instance, point to the increasing potential of symmetric participation and co-creation of content for democratisation of societies. Bruns

(2008a) advances this understanding of participation and co-creation, pointing to the dual and simultaneous role of individuals as producers and users in such networks; he refers to participating individuals as ‘produsers’.

Benkler’s (2006) rather optimistic account of the potentials of the Internet for the creation of a “networked public sphere” lies in his view that this new form of public sphere will dramatically change the inner workings of democratic systems, especially liberal democracies. He argues that

The Internet as a technology, and the networked information economy as an organization and social model of information and cultural production, promise the emergence of a substantial alternative platform for the public sphere. (Benkler, 2006, p. 177)

40 Literature Review For Benkler, this alternative platform means that engaged citizens will cooperatively provide their opinions, and “serve as a watchdog over society on a peer-production model” (ibid.). The emergence of a networked public sphere potentially makes every citizen a speaker, rather than a mere listener or voter.

With this ease of communication, individuals turn from passive agents into active participants in a conversation (Benkler, 2006, p. 213).

Similar views to Benkler’s are shared by Shirky, who posits that

[a]s the communications landscape gets denser, more complex, and more participatory, the networked population is gaining greater access to information, more opportunities to engage in public speech, and an enhanced ability to undertake collective action. […] these increased freedoms can help loosely coordinated publics demand change. (Shirky, 2011, p. 29)

Benkler and Shirky’s optimistic and techno-utopian argument, however, lies in what Mouffe (2013) views as one of the main paradoxical mistakes of liberal democratic thought today. Such an argument is primarily built on the assumption of individuals as rational agents who, once offered a promising tool such as the Internet, will engage in rational deliberative conversations, effectively forming a Habermasian public sphere. In other words, the consequentialist fallacy in such arguments is that the mere potentials and promises of the Internet are equated to a guarantee of positive consequences.

However, as Mouffe (2013) argues, this view effectively ignores the ineradicable role of antagonism. For Mouffe, this thought is “characterized by a rationalist and individualist approach” which, in order to thrive, “has to negate the political in its antagonistic dimension” (Mouffe, 2013, Chapter 1). However,

Mouffe argues that

the political in its antagonistic dimension cannot be made to disappear by simply denying it or wishing it away. This is the

Literature Review 41 typical liberal gesture, and such negation only leads to the impotence that characterizes liberal thought when confronted with the emergence of antagonisms and forms of violence that, according to its theory, belong to a bygone age when reason had not yet managed to control the supposedly archaic passions. (Mouffe, 2013, Chapter 1)

Two important points arise from Mouffe’s argument (above): first, the confrontation with the emergence of antagonisms; and, second, Mouffe’s introduction of the issue of passions into the debate. The former is a pressing issue with regard to political communication on social media platforms, which has increasingly been referred to as ‘polarisation’, ‘fragmentation’,

‘balkanisation’, ‘filter bubbles’, and ‘echo chambers’. As discussed later in this chapter, such phenomena intrinsically arise from the antagonistic dimension of the political. Furthermore, the role of passions raised here by Mouffe, is also a significant factor in communications on social media, and I return to this topic later in this chapter.

The optimistic views discussed above, however, are not shared by many other scholars. From the point of view of the labour that individuals put into the creation of content and participation in new media, some—such as Fuchs

(2010, 2013, 2014) and Terranova (2004)—point to the inherently exploitative nature of social media platforms. In their account, the free labour of social media users only leads to the platform owners’ accumulation of more power, capital, and control. The irony of such a situation—which Fuchs (2013, p. 117) refers to as “digital playbour”—lies in the fact that users actually enjoy this exploitation.

From another perspective, others point to the narcissistic, cynical, and somehow nihilistic nature of activity on social media platforms (Lovink, 2011).

For Lovink, the “promise of communication as an exchange”, which “lure[s]

42 Literature Review users to say something, anything”, is merely an “emancipatory gesture”. On the other hand, he argues that this (digital) awareness of the Other is not an

“actual, real, existing interaction” (Lovink, 2012). He, rather harshly, frames this as a “clever trick” of social media:

The silence of the masses that Baudrillard spoke about has been broken. Social media has been a clever trick to get them talking. We have all been reactivated. The obscenity of common opinions and the everyday prostitution of private details is now firmly embedded in software and in billions of users. (Lovink, 2012)

While I do not necessarily share Lovink’s view, I do believe that he raises an important point with regard to the logics of social media platforms. As discussed later in this chapter, a primary logic of the platforms (van Dijck &

Poell, 2013) is to attract as much online user activity as possible. This, of course, leads to a blurring of the borders between the ‘private’ and the ‘public’ spheres. However, I follow the line of optimistic and pessimistic views of social media here, and return to this point later (see Section 3.4).

Returning to the issue of participation, on which both optimistic and pessimistic arguments rely, another question to direct at both views is whether or not this participation actually occurs on social media platforms. In general, the short answer seems to be ‘not as much as we think’, since the majority of users on platforms consist of ‘passive spectators’ and ‘inactives’ (van Dijck,

2009). On the other hand, “[n]otions of ‘participatory culture’ tend to accentuate the emancipation of the engaged citizen, who unleashes her need for self-expression and creativity onto the digital spaces created expressly for this purpose” (ibid.).

Evgeny Morozov’s arguments perhaps best summarise the range of criticisms directed at hyper-utopian accounts of social media platforms. His general

Literature Review 43 critique of techno-utopian views stems from what he refers to as the “Google

Doctrine”, which is “an enthusiastic belief in the liberating power of technology accompanied by the irresistible urge to enlist Silicon Valley start-ups in the global fight for freedom” (Morozov, 2011, p. xii). He, again rather polemically, sums up the Google Doctrine in the following quote:

Opening up closed societies and flushing them with democracy juice until they shed off their authoritarian skin is just one of the high expectations placed on the Internet these days. (Morozov, 2011, p. 19)

In the previous sections, I focused—quite intentionally—on the extreme ends of the spectrum of views about the inter-relationship of social media and democracy. However, I am in agreement with Dahlgren (2013) in believing that both sides of the spectrum provide essentialist theorisations. Neither cyber- utopianism nor cyber-scepticism can provide satisfactory answers to the question of whether/how social media play a role in democratic projects. In the following section, I opt for another route: I review studies that take a more complicated relationship between the two as their starting assumption.

3.3 Public Sphere, Democracy, and Social Media

Torfing points out that the notion of democracy itself is an empty signifier, so

“overflowed with meaning” and articulated in so many different ways that it is practically a signifierwithout a signified (Torfing, 1999, p. 301). Within the disciplines of democratic theory and political studies, one can see a great deal of disagreement on what is—or should be—a democracy, and on which democratic model is the most promising, efficient, or practical. However, this disagreement and difference of opinionhas not so much been transferred to media and communication studies. Although there is a large body of literature that discusses the inter-relationship of media and democracy, it seems that

44 Literature Review Habermas’s public sphere theory often—implicitly or explicitly—plays the dominant role in the literature (Ess, 2018).

This, of course, does not mean that this theory is uncriticised or blindly accepted. Rather, it is used as a theory, model, framework, or standard against which different phenomena are assessed. Broadly speaking, such assessments are either theoretical engagements with Habermas to investigate whether

(traditional, new, digital, social) media can be places where the public sphere can form (Dahlberg, 2007, 2013), or normative investigations of different media phenomena to assess how close we are, or how close we should be, to the ideal public sphere (Kies, 2010).

At the theoretical level, Habermas’s theory of the public sphere has received criticism for its reliance on rationality, idealism, and/or exclusivity. From economic and feminist perspectives, the public sphere theory is argued to be based on a bourgeois, male-dominant ideal that practically excludes minorities, women, and lower classes (Ess, 1996, 2018; Fraser, 1992; Papacharissi, 2010).

Furthermore, others argue that the public sphere theory is essentially an idealist and normative approach and that, ever since its inception, it remains as merely an ideal (Downey & Fenton, 2003; Fenton, 2018; Warner, 2002;

Webster, 2013). Finally, the assumption of rationality is also argued to be in contradiction to the realities of political life. Smith (2017) summarises the idea and ideals of deliberative democratic thought:

Deliberative democrats, such as Habermas and Rawls, posit disagreement as an institutional flaw which can be solved by better institutions. Disputes are merely a matter of temporary miscommunication which, through rational deliberation, can be solved in a way such that all parties involved can be satisfied with the outcome. (T. G. Smith, 2017, p. 106)

Literature Review 45 Smith—building on Mouffe—argues that the failure of such conceptualisation lies in the fact that plurality and consensus are in no way compatible, in that any consensus is “the expression of a hegemony and thus generates exclusion”

(T. G. Smith, 2017, p. 109).

For Mouffe (1999, 2000b), the paradoxical nature of deliberative democracy presents itself in two ways. First, she argues that the achievement of a fully inclusive rational consensus is an impossibility, since “power is constitutive of social relations” (Mouffe, 1999, p. 24). However, the deliberative model of democracy is built on the denial of the existence of power relations in its public sphere, and assumes an equal role for all participating citizens. In this way,

“this model of democratic politics is unable to acknowledge the dimension of antagonism that the pluralism of values entails and its ineradicable character”

(ibid.).

Building from this argument, the second paradoxical level of the deliberative democratic model, and its reliance on achieving consensus, lies in the fact that once this consensus is achieved, there is no further need for deliberation and decision making, since what will be left is a closed, hegemonic system, in which only the hegemonic principles are repeated (Mouffe, 2000b). Briefly put, the paradox of deliberative democracy is in the fact that upon achieving its goals, it will eliminate itself and lead to no further progress. In Smith’s words, in such a situation,

No politics would be needed as the sameness of everyone would mean that no decision would be controversial and, instead of decision making, there would simply be an enacting of the consensus view. (T. G. Smith, 2017, p. 112)

Other scholars, such as Fraser (1992), challenge the singular public sphere from another perspective, yet point to similar arguments. For Fraser, the public

46 Literature Review sphere should not be viewed as one unified public, but rather as a multitude of publics, some of which she views as counter-publics; that is, publics with views against the hegemonic public sphere.

Apart from the theoretical criticisms directed at the public sphere theory, both the original, singular Habermasian conception of the public sphere, and the arguments favouring Fraser’s view of multiple publics and public spheres, have found an important place in new/digital media studies. As Dahlgren (2005) points out, questions about the role of the Internet in the empowerment of citizens and the development of democratic models based on public debate have been a matter of academic argument for more than a decade. At present, and over a decade after Dahlgren’s note, such questions still attract academic attention, especially given the ubiquity of social media platforms. Fenton

(2018) believes that studies with answers in favour of the positive role of the

Internet in democracy generally rely on Habermas’s theory:

Any discussion of digital media and communication and their roles in enhancing democracy and political participation frequently falls back on Habermas' concept of the public sphere. (Fenton, 2018, p. 29)

However, Fenton finds this conception problematic, not only because of its technologically deterministic assumption that better media automatically mean a better democracy, but also because such views derail a “critical gaze away from the institutional arrangement of liberal democracy itself” (Fenton, 2018, p. 29). Papacharissi (2002) also criticises such a utopian rhetoric about new technologies and their role in the democratisation of societies.

Literature Review 47 3.4 The Fragments of a Broken Public Sphere

As already discussed, many of the studies and works related to social media and democracy rely on one form or another of aggregate, deliberative, and liberal theories of democracy, especially the Habermasian public sphere (Ess,

1996, 2018; Fenton, 2018; Papacharissi, 2010). The question is, therefore: If the unified and singular public sphere theorised by Habermas is a theoretical impossibility (Fraser, 1992; Laclau, 2007; Mouffe, 1993, 2000b; Webster, 2013), or if the Internet has not been able to lead us to such a space (Dahlberg, 2007;

Lovink, 2011; Morozov, 2011), how should we envisage the realities of online communicative spaces? That is, if the realities of communication environments point to a fragmented public sphere, a multitude of online publics and counterpublics, or a Balkanisation of society, what theoretical standpoints can explain these phenomena?

Scholars who take a more empirical approach to studying online communication spaces, theorise these phenomena from various—often overlapping—perspectives. A common starting point in this line of thought is the fragmentation of the public sphere online (and offline). In later reformulations of his theory, Habermas (2006) himself acknowledges the presence of fragmentation, and introduces “issue publics” into his original theory. However, he maintains his normative and idealist stance regarding the theory of public sphere, arguing that “the overlap of issue publics may even serve to counter trends of fragmentation” (Habermas, 2006, p. 422).

Focusing on the topic or general interests that form a public sphere, Webster

(2013) and Dahlgren (2009) argue for the presence of ‘political public spheres’.

For Webster, ‘the’ public sphere, in the way envisaged by Habermas, is primarily built on the assumption that a ‘good’ citizen is a well-informed,

48 Literature Review engaged individual who actively engages in political deliberations (Webster,

2013, pp. 31–32). The public formed by a collective of these good citizens, therefore, is a political public sphere. A similar argument is put forward by

Hartley and Green (2006), who theorise a ‘cultural public sphere’ that is formed around identity rather than politics per se. For them, the cultural public sphere is a medium between culture and politics, while ‘the’ public sphere, in its original formulation, is “a convenient fantasy” (ibid).

The fragmented public sphere is conceptualised as a collective of publics— including dominant and peripheral publics—by Downey and Fenton (2003), who take the issue of power relations into account in their theorisation. For them, one can imagine a dominant public sphere—which, in discourse- theoretical terms, could be conceptualised as the hegemonic discourse—and a multitude of counter-public spheres, formed around a shared interest to introduce their discourse into the dominant public sphere and become part of the hegemonic discourse. A counter-public sphere, therefore, is “the public sphere of the dominated” (Downey & Fenton, 2003, p. 188). These non- hegemonic publics—viewed as counter-public spheres by Downey and Fenton, or as subaltern counterpublics by Fraser (1992)—are theorised as ‘public sphericules’ by Cunningham (2001), who conceptualises them as “social fragments that do not have critical mass” (p. 134).

With specific attention to multiple publics and public spheres in the online communicative environment, other scholars conceptualise them with regard to the more materialistic dimension of the technology on which they are formed.

Benkler’s (2006) “networked public sphere” is such an example. Bruns and

Highfield (2016) also trace such views in the use of terms such as ‘blogosphere’ and ‘Twittersphere’. However, they argue that “technocentric definitions of

Literature Review 49 specific public spheres may not be particularly productive” (Bruns & Highfield,

2016, p. 106).

Within the context of social media platforms, a recurring theme is the disappearance of boundaries between the traditional conceptions of what constitutes ‘private’ or ‘public’. Papacharissi (2010) points to these faded boundaries, arguing that in such spaces, one can no longer draw a distinct line between the private and the public. Rather, users engage in “privately public” and “publicly private” interactions online (Papacharissi, 2010, p. 61). For

Schmidt (2014), these disappearing boundaries can be seen in the form of overlapping “personal publics”, as communities shaped around an individual online (e.g. Twitter followers, Facebook friends). In turn, these potentially connect the individual to the broader communication environment. This intermingling of the private and the public even finds its way into the spaces traditionally viewed as solely public. Highfield (2016) uses the example of a citizen’s private engagement with the public sphere of politics in the form of selfies in a voting booth. He argues that in such a situation, one cannot draw the boundary between where the public ends and the private starts, or vice versa. These social media spaces, which have not been originally designed to be spaces for politics, turn into “third spaces”, being neither public nor private, but something in between (Wright, 2012).

This brief and descriptive summary of the various theorisations of a fragmented public sphere—especially online—points to different perspectives, each emphasising a unique aspect of fragmented public spheres. However, a number of key characteristics are common to all. The first common feature is the treatment of the fragmentation of the public sphere itself as a common ground in all the aforementioned theories. The starting point of enquiry for these

50 Literature Review scholars, therefore, is their shared understanding of a communication environment that cannot be approached as a singular Habermasian public sphere. However, the distinguishing aspect of the theories, and what sets them apart, is their description and interpretation of the fragments in the public sphere, or precisely, their understanding of what each fragment constitutes. In this sense, the theoretical goal for these theorisations is the provision of an explanatory framework or analytical construct, to better understand the nature of publics online. What remains underdeveloped or unexamined at times, however, is the why question. The notion of a fragmented public sphere marks the starting point of the above theorisations, and they provide detailed insights into the nature and dynamics of the fragments. However, there is little engagement with why the fragmentation occurs in the first place.

The second common feature in the aforementioned theories is their emphasis on the role of the material affordancesof online spaces, particularly social media platforms, in (re-)shaping the various publics online. As a “genre of

‘networked publics’”, social networking sites are “restructured by networked technologies” (boyd, 2011, p. 48). This conceptualisation does not limit the understanding of networked publics as merely an artefact of technological affordances of social media sites. Rather, boyd emphasises the point that although the affordances of network technology play an important role in configuring the space in which communication takes place—which, in turn, shapes the engagement of participants—these affordances do not necessarily dictate participants’ behaviour. Therefore, networked publics created by social media platforms “are simultaneously a space and a collection of people” (ibid. p. 50). From a discursive-material perspective, this notion of networked publics allows for the non-hierarchical relationship between agency and material. Boyd also introduces the role of underlying social structures and discourses in her

Literature Review 51 formulation of social media sites as networked publics, arguing that “networked publics appear to reproduce many of the biases that exist in other publics”, such as social inequalities and stratification around race, gender, or sexuality

(boyd, 2011, p. 63).

A third key point common to the theorisations of fragmented public spheres is the disappearance of the boundaries between the traditional understandings of

‘private’ and ‘public’. Although (as already shown) different scholars articulate these fading boundaries in various forms, this theme remains a common feature.

Papacharissi dedicates a large body of her research to the investigation of this phenomenon. Writing in 2002—before the emergence of the massive social media platforms as we know them today—Papacharissi argues for a radically different public sphere:

the internet may actually enhance the public sphere, but it does so in a way that is not comparable to our past experiences of public discourse. Perhaps the internet will not become the new public sphere, but something radically different. This will enhance democracy and dialogue, but not in a way that we would expect it to, or in a way that we have experienced in the past. (Papacharissi, 2002, p. 18)

In a later work, Papacharissi (2010) delves more deeply into the disappearing boundaries between the private and public spheres, and their implications for both the everyday life of citizens and democratic thought. She uses the concept of a ‘private sphere’ as a metaphor for a new phenomenon in which the private, the public, the social, technology, practices, and spaces have converged. For

Papacharissi, the private sphere is situated within the realm of the social, as

“an alternative to the bipolar continuum of public and private”, which has elements of both, but is not subsumed by either (Papacharissi, 2002, p. 49).

52 Literature Review With regard to democracy, she places an emphasis on the role of deliberation as central to any democratic model, albeit the fact that she criticises the

Habermasian notion of deliberation, which is inherently built on exclusionary and rationalist bases. Rather, she calls for a shift in our understanding of deliberation, and sees it as a form of opinion exchange (p. 20). For

Papacharissi, within the realm of social media—and with the convergence of the political, the public, the private, and the social—this opinion exchange can potentially lead to an agonistic pluralism, in the way conceptualised by Mouffe

(Papacharissi, 2010, pp. 157–167).

Another important point in Papacharissi’s formulation of opinion exchange online is her emphasis on distancing ourselves from the traditionally hierarchical and dichotomous view of rationality and affect. For Papacharissi

(2015, 2016), affect plays a major rolein networked publics. She views affect as “the sum of—often discordant—feelings about affairs, public and private,

[…] as the energy that drives, neutralises, or entraps networked publics”

(Papacharissi, 2015, p. 20). She questions the traditional Western hierarchical understanding that positions rationality at a higher level than affect, and that limits deliberation—as Habermas does—to rationality only. In a social media environment, she shows how networked publics form through a collective affect, effectively creating “affective publics”. In her view, it is not merely rationality that creates the political identities of citizens; rather, people “feel their way into politics” (Papacharissi, 2015, p. 25).

From a discourse-theoretical perspective, Mouffe (2000a) also questions the dualistic and hierarchical view of liberal democratic thought. She argues that rationalism is “blind to the nature of the political and denies the central role that passions play in the field of politics” (ibid.). Affect/passions themselves,

Literature Review 53 of course, are always contingent structures that are informed and influenced by the plurality of discourses in the field of discursivity. Within the discursive- material knot, passions/affect—as one’s feelings about affairs—are influenced by the discourses within which one is situated. In this sense, passions can be seen as heuristics for discourses and structures.

3.4.1 A Discursive-Theoretical Re-Reading

Although the various scholars and theories reviewed in the previous sections do not all explicitly refer to the different dimensions and aspects of the discursive-material knot, traces of the significance of these different aspects are evident in their writing. Whatever definition of ‘a public’ is used, it is clear that publics formed online are always reliant on the technological affordances of the Internet as a whole, and on the particular affordances of the platforms.

The imagined audiences created by these networked publics are themselves embedded in different discourses. Thevery existence of publics, whether praeter hoc, ad hoc or post hoc (Bruns & Burgess, 2015), topical or affective

(Papacharissi, 2016), is reliant on the material affordances of a platform. At the same time, how one reacts to this public depends on one’s identification with particular discourses. These reactions, themselves, are again manifested through the materialities of the platform, and further influence the public itself, and the discursive structures on the platform.

As previously shown, the different conceptualisation of publics, fragmented public spheres, and online communities (either imaginary, collective, or connective) all start from the assumption and observation that ‘the’ public sphere is, in fact, fragmented. From a discourse-theoretical perspective, this fragmentation is of course to be expected, given the ineradicability of antagonism (Laclau & Mouffe, 1985). How, and with which discourses, an

54 Literature Review individual identifies, situates them in particular publics/discourses. This situation, in turn, creates a multitude of publics and public spheres, each with its own interests, genres, and modes of engagement.

The ineradicability of antagonism (i.e. radical negativity) in the process of identification, effectively means atth even in a set of interconnected discourses—such as discourses around sports, or politics—one cannot find a unified, coherent, and singular publicsphere. Rather, there is always a multitude of discourses, stabilised through common nodal points, and struggling to achieve hegemony. In the realm of politics, for instance, the connectivity afforded by networked publics provides the freedom and ease for citizens to identify with discourse communities and publics online. In this space, therefore, the individual chooses whom to follow, what to retweet, what hashtags to use, and so on. Each of these decisions puts the individual in a different networked public, effectively creating a multitude of publics and public spheres. This process is observed as ‘fragmentation’ in the studies reviewed above. The individual choices made by online users, of course, are themselves influenced by, and constitute the broader discursive structures.

So far, I started from a macro theoretical level by discussing the high theories that form the primary sensitising concepts of the study; namely, discourse theory and discursive-material analysis (Chapter 2). I then moved to a brief engagement with middle-range theories that investigate the inter-relationships between the Internet—particularly the participatory web—and democracy.

Given the fragmentation of the public sphere (as argued by different scholars),

I reviewed the various conceptualisations of a fragmented public sphere, and viewed it from a discursive-material perspective. In the following section, I move to a more fine-grained level, briefly reviewing some of the studies of the

Literature Review 55 different aspects of the discursive-material knot that inform the tertiary sensitising concepts of this study.

3.4.2 Social Media Materialities and Discourses

Debates around the role of the materialities of technology in shaping human life have long been a key recurring theme in different disciplines. For the most part, media and communication scholars have moved—or at least called for a move—from deterministic accounts of the role of technology. Rather, they have opted for alternative views and perspectives, namely, the social construction of technology (SCOT), and ‘mild’ or ‘hard’ social constructivist approaches

(Lievrouw, 2014). The primary question for most of these approaches, therefore, no longer concerns the impact of media on people, but rather, people’s appropriation of technology and how this reconstructs society and publics. Of course, in studies that attempt to answer this question, we still observe an oscillation between views that privilege one side or the other of the debate (i.e. material vs human agency).

Gillespie (2014) investigates the role of algorithms and their inner workings, arguing that ‘public relevance algorithms’, through their mathematical logics, select and find information to be shown to users. This information, in turn, positions the user in certain calculated publics by exposing them to a subset of knowledge deemed to be relevant to them through the mathematical calculations in the black box of algorithms. However, Gillespie’s conclusion with regard to the effects of this algorithmic curation of knowledge and information, eventually takes a more technologically deterministic side that privileges the material and its agency over the users. He argues that “[t]hese algorithms, which I’ll call public relevance algorithms, are—by the very same mathematical procedures—producing and certifying knowledge” (Gillespie,

56 Literature Review 2014, p. 168). Gillespie’s privileging of the material over agency is evident in his ascription of knowledge production to the algorithm. However, he does not delineate how—and why—the curation of pre-established and pre-produced knowledge can, and should be considered as production of knowledge, in the strict sense of ‘production’.

An alternative line of thought with regard to materialities of platforms— implicitly or explicitly—relies on ‘domestication theory’ (Silverstone, 2005,

2006), which focuses on users’ appropriation of the affordances. Within this mode of enquiry, technology, communication and users are viewed in an intertwined and inseparable way (Lievrouw, 2014). This is similar to the approach taken in the present research, which draws from the theorisation of the discursive-material knot (Carpentier, 2017).

In a similar way to the concept of ‘invitation’ developed by Carpentier (2017,

2019), Hutchby (2001) draws from conversation analysis to argue for a similar process that is involved in the way that people interact with the materialities of technology. For Hutchby, a material affordance acts as an invitation to a conversation. It does not necessarily force the user to take a particular action, but rather invites them either to use their turn and interact with the technology, or to remain silent and finish the interaction:

Thus, the invitation already provides the structural possibilities for the next move in a conversation: you may choose to respond or not, but whatever you do can be heard as an action in response to the affordances offered by the prior action. (Hutchby, 2001, p. 450)

Although this second approach has become a de facto standard mode of thinking about social media platforms and practices on them, different studies tend to still privilege one dimension over another, depending on the discipline in which the researcher is operating and (sometimes) the object of study.

Literature Review 57 A level of complexity with regard to the concept of affordances vis-à-vis social media platforms lies in the multifaceted and complex layers of affordances behind, on, and beyond social media platforms. Each platform has its own platform-specific affordances (Bucher & Helmond, 2018), economic underpinnings (van Dijck & Poell, 2013), and types of relationships between its users (van Dijck, 2013). Furthermore, the very logics behind the creation of platforms mean that they afford different things to different actors. This not only means a multilayered meaning of ‘affordances’, but also a multilayered meaning of ‘users’ themselves. With regard to affordances, platforms provide different affordances to their end-users; to those using their APIs (e.g. researchers, application developers); to advertisers; and so on. These different affordances intrinsically change our understanding of what a ‘user’ means.

Bucher and Helmond (2018) call for a shift in our view of the concept of user as either end-user or designer, to include every actor engaging with the platform in one way or another. Additionally, they argue that the users’ engagement with the affordances of the platform creates a ‘feedback loop’; this, per se, changes the structures of the platform, and practices on it.

With regard to users’ appropriation of technology, different scholars point to the way in which users interact with the technological affordances of platforms in order to achieve particular discursive goals. In a study of a race-based controversy in Australia, Matamoros-Fernández focuses on how users’ appropriation of the affordances of theplatform led to the amplification of racist discourses. She shows how users engaged with the affordances of the platform to hide their racist content from the moderators of the platform, and to distribute their racist content to more users (Matamoros-Fernández, 2017).

Furthermore, she focuses on the vernacular uses of the platform—for example, the use of humour and jokes—to show how users employ these vernaculars to

58 Literature Review amplify antagonisms. Similarly, in a study of racism against the Roma on

Facebook, Breazu and Machin (2019) identify similar strategies for the amplification of racism online. They find that “the affordances of Facebook open-up the possibility of mixing of humour, venting of frustration, extreme racism and sexual violence” (Breazu & Machin, 2019, p. 15) through practices such as the use of emojis, and the posting of humorous comments. Others focus on the use of fake profiles on Facebook to amplify Islamophobia (Farkas, Schou,

& Neumayer, 2018).

Such accounts of the engagement of users with the platforms, therefore, bring us back to the concept of ‘invitation’. Affordances such as the ability to mark content as ‘sensitive’ on Twitter, hence hiding it from being easily visible, privilege certain actions over others, and ‘invite’ users to interact with them in certain ways. However, the way a user eventually ascribes meaning to the affordance, and how they interact with it, has more to do with the users’ agency and identification with discourses, rather than the materiality of the affordance itself. As Milner’s (2013) study of platforms such as 4chan and reddit shows, although the ‘logic of lulz’ and humorous activities online can potentially be used to amplify antagonisms, they have the same potential for agonistic pluralism. A mere focus on the negative aspects of the appropriation of platform affordances, however, cannot satisfactorily account for their collective use in non-racist, non-sexist, or ‘everyday’ uses. As KhosraviNik (2017a) argues, it is not productive to think of such phenomena in causal ways, and to see platforms as the cause of problematic discourses or filter bubbles and echo chambers.

Returning to the theoretical framework of the study (Chapter 2), I view the production of knowledge and discourse as a solely human-centred aspect of

Literature Review 59 communication. Therefore, although I agree with the conception of users’ appropriation of affordances, I am wary of conclusions that privilege the material in the production of (problematic) discourses online and offline. Such conclusions generally tend to lean more towards the technologically deterministic side of the debate, in that they equate mere exposure to particular content with an unquestioned acceptance of the discourse. Such an understanding is problematic in a number of ways. First, it does not leave any room for agency on the part of the individuals, either at the level of discourse production or discourse consumption. In contrast, scholars such as Crawford

(2016) and Gerrard (2018) point to the very active role of users in ‘gaming the system’. Although users generally do not know how an algorithm works, they creatively find ways to subvert its working to achieve their purpose. Therefore, an essentialist view that conceives a fixed effect for algorithms cannot account for users’ agency. Rather, it views users as mere receivers of information, directly influenced by what they see in the media. Simply put, exposure to content is equated with internalisation of discourse, with no regard for users’ agency in thinking about, accepting, or rejecting it. Such a view is reminiscent of the magic bullet theory once dominant in media studies (Hilbert, Vasquez,

Halpern, Valenzuela, & Arriagada, 2016).

Another problem with the technologically determinist mode of thought about the effects of social media materialitiesin the production of discourse, shows itself in the reliance of this thought on a normative, rationalist, liberal ideal; this ideal effectively equates the removalof symptoms with the elimination of diseases. Such a view is evident in the use of medicalised and rationalist words such as ‘civility’ or ‘health’ by the platforms themselves (see, for instance,

Twitter, 2018). In other words, as discussed in the previous sections, this view essentially argues that a better technology makes a better society (Fenton,

60 Literature Review 2018). In this sense, the dominant view of this sort is that once problematic discourse is eliminated from social media platforms, we can—and will—have the liberal consensus we are looking for. Such a view, however, does not account for the ineradicability of antagonism.

While there is a need for more transparent content moderation practices by platforms, and the removal of explicitly problematic content (e.g. calls to physical violence, terrorist propaganda, child pornography), it is worth noting that from a discourse-theoretical perspective, any moderation practice, by itself, is an indication of an exclusionary act, potentially eliminating the discourse one disagrees with (i.e. inimical antagonism). In this sense, moderation practices are themselves hegemonic and political acts, privileging certain discourses over others. Therefore, aside from the moderation of content related to physical violence, an over-emphasis on moderation, in effect, increases the likelihood of the emergence of a communicative space built on identity politics. In such a space, the elimination of the Other-enemy finds a much more privileged status than the move to a more democratic society. In this regard, Mouffe points to the inherent dangers of such a space:

A well-functioning democracy calls for a vibrant clash of democratic political positions. If this is missing there is the danger that this democratic confrontation will be replaced by a confrontation among other forms of collective identification, as is the case with identity politics. Too much emphasis on consensus and the refusal of confrontation leads to apathy and disaffection with political participation. Worse still, the result can be the crystallization of collective passions around issues which cannot be managed by the democratic process and an explosion of antagonisms that can tear up the very basis of civility. (Mouffe, 2000b, p. 104)

This position toward content moderation becomes particularly problematic given the discourses and moderation logics of the platforms, which operate

Literature Review 61 primarily under capitalist logics and ideologies (Dean, 2005; Fuchs, 2014; Gehl,

2014), and are almost exclusively located in Silicon Valley. As Mark

Zuckerberg—the CEO of Facebook—claimed in the United States Senate,

Silicon Valley is an “extremely left-leaning place” (“Transcript of Mark

Zuckerberg’s Senate hearing,” 2018). In effect, this stance could potentially increase the likelihood of moderation of right-leaning, conservative voices. This, in turn, will lead to more reactions and radicalisations by right-wing ideologies, and effectively lead us back to the very thing (if not worse) we are trying to avoid through moderation.

3.5 Horizontal and Vertical Contexts

An important consideration in conducting a study of data obtained from social media platforms is the intrinsic complexities of such data. While early research in computer-mediated communication tended to resort to one form or another of ‘digital dualism’ (Jurgenson, 2011) that separated the online/virtual from the offline/real, this view is no longer a dominant perspective in media and communication studies. Although scholars are aware of the limitations of social media data—particularly regarding its generalisability to the society at large— a distinct boundary between the online and offline no longer differentiates the data obtained from social media platforms. This, of course, does not mean that social media data can only answer questions about social media users. Rather, one can still make numerous inferences about society from such data (Murthy,

2016).

Another critical consideration in this regard, however, is that simply because researchers have access to large amounts of available data, they are no closer to an ideal of ‘objectivity’. That is, while the vast amount of data available to researchers—the so-called ‘big data’—allows for more quantitative,

62 Literature Review mathematical tools and methods, it is a mistake to draw any conclusions with regard to the objectivity of such data and methods. This is especially the case when it comes to arriving at conclusions regarding the meaning of the data, and what can be concluded from it (see, for instance, the special issue in

International Journal of Communication: Andrejevic, 2014; Bowker, 2014;

Busch, 2014; Crawford, Miltner, & Gray, 2014; Driscoll & Walker, 2014;

Puschmann & Burgess, 2014; Thatcher, 2014). In studies that investigate social media big data, context plays a significant role, and de-contextualising datasets is potentially a misleading practice (boyd & Crawford, 2012). Therefore, even if quantitative methodologies are used to make sense of the large datasets, what is inseparable from any study of social media data is the role of a human interpreter in drawing conclusions from the data (Manovich, 2012).

With regard to the context itself, however, we still face a multilayered and complex landscape. Apart from issues such as the ahistoricity of content online

(KhosraviNik & Unger, 2015), there remain the questions regarding the different contextual layers. If, as discussed, we cannot and should not draw distinct lines between the online and the offline, and should instead opt for the view that social media discourses are in fact deeply embedded within the society itself, we are faced with a number of key challenges regarding context, and what constitutes context.

Even though social media platforms and communicative practices on them are embedded in the broader societal contexts (KhosraviNik, 2017b), the role of the materialities and affordances of platforms themselves cannot be ignored. I argued in the previous sections that while my discourse-theoretical orientation means that I view meaning-making practices, knowledge production, and discourse production as purely anthropocentric phenomena (see also

Literature Review 63 Carpentier, 2017, p. 7), I concurrently emphasise the role of the material in shaping and re-shaping articulatory practices and networks. Within the context of social media platforms and articulatory practices on them, the material manifests itself in affording particular discursive formations on the platform.

Nevertheless, these structures are themselves embedded in the broader discursive structures; are influenced by the agency of users articulating them; and, in turn, constitute and constrain those very same structures, discourses, and agencies.

In order to be able to account for both levels and layers of contextual peculiarities of social media platforms, I separate them into ‘horizontal’ and

‘vertical’ contexts4. This, by no means, implies that the horizontal and vertical contexts are separate entities. Rather, it is an abstract separation to facilitate the discussion. Within the vertical context, we are faced with broader power relations in the society; social structures; local and global discourses; institutions; and actors. In terms of the discursive-material knot, the vertical context, therefore, has to do more (not exclusively, however) with discourse and structure than agency and material. A user who is producing or distributing a text on a social media platform is not performing this act in a vacuum: They are relying on, and drawing from social structures and discursive formations, and their identification with them. In this sense, the produced text is, in effect, a computer-mediated discourse (Herring, 2004, 2013).

4 The idea for the use of the words ‘horizontal’ and ‘vertical’, of course, is not something that I have personally conceived. Laclau (2005) refers to horizontal chains of equivalence. Regarding social media, KhosraviNik (2017b) and KhosraviNik & Esposito (2018) also incorporate the concept of horizontal and vertical contexts. However, in a slightly different way than used here, they refer to industrial (horizontal) and social (vertical) contexts. Couldry (2012) also uses the term ‘horizontal network’ as ‘communication outside elites’, in differentiating it from the social practice.

64 Literature Review The role of power is also significantwith regard to the vertical context.

Although the optimistic accounts discussed earlier in this chapter emphasise the role of symmetric power relations and an equal distribution of power enabled by the participatory web—in which every citizen has an equal voice— empirical evidence suggests that vertical structures and power relations still play an important role in defining what happens online (e.g. Cha, Haddadi,

Benevenuto, & Gummadi, 2010; Dubois & Gaffney, 2014; C. S. Park, 2013;

Rogstad, 2016). Furthermore, the production of discourse in itself relies on the structural power relations behind discourses (KhosraviNik & Esposito, 2018)— the power relations that shape what/how discourses are produced and/or consumed. The traditional elites—such as politicians, journalists, opinion leaders—therefore, still play a major role in shaping online discussions and computer-mediated discourses.

Horizontally, we can conceive power relations, structures, discourses, and information flows shaped by and on social media platforms, more or less independent from the vertical structural contexts. The key in the previous sentence, of course, is the phrase more or less. Although the horizontal and vertical contexts can never be separated, the particularities of social media platforms afford new forms of discursive structures and power relations. In short, although social practices are not necessarily platform-born, some practices are particularly platform-borne; that is, they are enabled and influenced by, and benefit from the affordances of the platform. One example of such is what Papacharissi calls ‘crowd-sourced elites’ (Papacharissi, 2015).

Although these elites still operate within the vertical contextual structures, their (communicative) power is given to them by the other users in the horizontal context of the social media platforms. Additionally, they strategically take advantage of the material affordances and logics of the

Literature Review 65 platform itself to accumulate further discursive power (Potts, Simm, Whittle,

& Unger, 2014). Another example of platform-borne discursive power and influence can be seen in the case of ‘micro-celebrities’, who rely on the affordances of the social media platforms to accumulate influence and power

(Abidin, 2015; Page, 2012).

Finally, another horizontally important aspect of context on social media platforms is the platforms’ networked affordances, which facilitate both the flow of information and the discovery of other users with resonant discourses

(Himelboim, McCreery, & Smith, 2013; McPherson, Smith-Lovin, & Cook,

2001; Papacharissi, 2011; van Dijck, 2013). In effect, this affordance constructs online clusters (assemblages) of users and practices sharing similar discourses.

These communities of discourse and/or practice, therefore, benefit from the horizontal affordances of the platform in that they can more easily reproduce and/or amplify their discourses in a hegemonic project.

Returning to a discursive-material rearticulation of these communities, this horizontal connectivity and these networked affordances can potentially be key factors in the formation of agonistic networks of discursive alliances. For Laclau and Mouffe, the formation of a democracy is dependent on a democratic subject, which has to emerge in a horizontally articulated chain of equivalence, against a vertical antagonist or a hierarchical power relation (Laclau, 2005, p.

171; Mouffe, 2013). The horizontal linkages (Bratich, 2011) envisaged by

Laclau and Mouffe can, therefore, rely on the technological affordances of social media platforms, which facilitate the construction of counter-hegemonic clusters of users and practices. Whether this does happen or not in practice, however, is a question that needs further investigation. What I have

66 Literature Review conceptualised as ‘inter-cluster dynamics’ in this study also includes this question.

In combining the horizontal and vertical contexts, the communication environment on social media platforms can be envisaged as a “patchwork of overlapping public spheres centred around specific themes and communities”

(Bruns, 2008b). Within this environment, each cluster of users and practices— referred to as public spheres by Bruns—consists of horizontal networked connections, which are themselves operating within the vertical context. Bruns

(2008b) uses a graphic visualisation of this environment to elucidate this point, wherein each small circle represents a public sphere with its own interests, opinion leaders, and elites, together operating in the vertical context.

Figure 3-1: Horizontal and vertical contexts (Bruns, 2008b) To account for both the horizontal and vertical contexts, and for both the deeper understanding of the dynamics of discursive struggles and the broader communication practices, one appropriate solution is an incorporation of mixed-methods approaches in a study of social media platforms (Murthy, 2016;

O’Halloran et al., 2016). In this way, one can benefit from the large scale and

Literature Review 67 broad insights that quantitative methods can provide and these, in turn, can point the research to patterns that are otherwise hidden (Manovich, 2012). At the same time, the in-depth, qualitative examination of data is also necessary to achieve the thick description (Geertz, 2008) often aimed at in discourse- analytical approaches.

3.6 Twitter: An Object of Study, and Much More

Defining what Twitter is, or even reaching a comprehensive typology of all the ways it is used, is a practical impossibility. The broadest and simplest definition of ‘Twitter’ is that it is a microblogging service. One can also consider Twitter simply as a material space, a tablet of sorts on which discourses, interests, opinions, feelings, and so on can be inscribed.

This digital tablet, of course, is never presented as totally blank to its users.

Any human-made material carries on the discourses of its creators (Murthy,

2018, Chapter 3). However, the user-inscribed discourses on this tablet also take various forms and shapes, and construct different assemblages. This takes us back to the conceptualisation of the context of communication on Twitter as being both horizontally and vertically oriented (Section 3.5). On the horizontal surface of this “digital object” (Murthy, 2018, p. 38), users inscribe their vertically constructed discourses, find other like-minded users, and use the material affordances of the platform to create communities of interest and practice that transcend geographical boundaries. These communities, however, are different from the traditional conceptions of a community as merely a collective of people (Kehus, Walters, & Shaw, 2010); rather, they are assemblages of discourses, users, and materialities.

68 Literature Review These assemblages—which I also interchangeably refer to as ‘clusters’—are exactly why it is impossible to reach a typology, or even a definition of Twitter.

The contingency of discursive formations means that users ascribe different meanings to different practices and materialities, at different times, and based on different purposes5. In turn, this makes it impossible, even futile, to attempt to comprehensively define Twitter. Briefly put, therefore, Twitter—or any other social media platform for that matter—is the historical sum of what its users want it to be.

Throughout its lifespan, Twitter has been used in different ways. It is shown that this platform is a useful tool for journalistic practices—whether professional or amateur citizen journalism—and a way for users to share and be aware of news (Bruns & Burgess, 2012; Gil de Zúñiga, Jung, & Valenzuela,

2012; Kwak, Lee, Park, & Moon, 2010; Orellana-Rodriguez, Greene, & Keane,

2016). Twitter is also proven to be a very effective tool in communication during crises such as floods and earthquakes (Bruns, Burgess, Crawford, &

Shaw, 2012; Hermida, 2010).

On the more banal side, Twitter enables the communication of ‘me, now’ statements, which simply share the whereabouts and/or feelings of a user with their followers, and with all other users of the platform. These performances of the self (Papacharissi, 2012) are appropriated as an effective strategy for celebrities to interact with their fans (Stever & Lawson, 2013); for ‘wannabe’ and micro-celebrities (Page, 2012) to accumulate fandom and following and become platform celebrities; and for professionals to represent themselves in carefully curated ways, as a self-branding practice (Hanusch & Bruns, 2017).

5 See also Marres (2014) and her discussion of ‘material participation’ and the contingency of media arrangements.

Literature Review 69 In the domain of politics, Twitter is used as an effective medium for political campaigning in elections, and as a way for politicians to circumvent traditional gatekeeping practices and engage directly with their electorate (Graham,

Broersma, Hazelhoff, & van’t Haar, 2013; Grant, Moon, & Busby Grant, 2010).

On the other hand, the bottom-up affordances of Twitter enable activists to find a platform to voice their opinions and dissent (Potts et al., 2014). Of course, the flipside of this very affordance is that problematic discourses also find a platform on which to amplify their voice (Evolvi, 2017; Jane, 2017;

Krämer, 2017). Twitter is also used as a medium for cultural conversations

(Brock, 2012); for minorities to increase their visibility; and for creating impermanent communities of bonding and ‘ambient affiliation’ (Zappavigna,

2011).

Finally, and perhaps the function that receives the most media attention, is the use of Twitter in protests and revolutions. This is despite the fact that

Twitter was initially hailed—in a causal way—as ‘the’ medium for the revolution and protests during the Iranian 2009 protests and the Arab Spring in the following year, and that later scrutiny showed that this initial hype was unwarranted (K. Clarke & Kocak, 2018; Cottle, 2011; Ess, 2018; Murthy,

2018). Rather, Twitter was not the cause, or even a driving factor, of the protests; it was merely a communication tool used by the dissidents to circumvent the state-controlled media, and in some cases, to coordinate protests.

These are but a few of the different ways that Twitter and its affordances are appropriated by users. What they share in common is their potential to create networked assemblages of users, practices, and discourses, based on a shared objective. Whatever the objective, and whichever affordance of Twitter is relied

70 Literature Review on, the resulting cluster is an inscription of discourses on the materialities of the platform. From a discursive-material perspective, therefore, the material affordances of Twitter—such as the etweetr button, @mentions, favourites, lists, ability to share URLs, and hashtags—all ‘invite’ users to interact in certain ways (Carpentier, 2019). How they interact and participate, and what meaning they collectively ascribe to the affordance, eventually creates assemblages. When combined, these contingent assemblages—even the assemblage of users who decide not to interact in any way (that is, ‘lurkers’)— define what Twitter is. For a researcher, therefore, the starting points of entry are the identification of the topic of esearch,r and the collection of these clusters

(by using their digital traces) to address the specific research questions.

3.6.1 Methodological Approaches to Studying Twitter

The broad nature of content created on Twitter allows for a wide range of approaches, tools, and methods from many and varied disciplines. This leads some scholars to suggest general metrics for the study of Twitter. Through a literature review of Twitter studies that focus on online social behaviour, three sets of metrics emerge that appear to be able to provide useful insights about the large datasets collected from Twitter (Aarts, van Maanen, Ouboter, &

Schraagen, 2012). The authors of the paper classify the literature into metrics that are developed to study the characteristics of actors, messages, and networks.

As the “killer app” of Twitter (Bruns & Burgess, 2015), hashtags are a primary source of data collection for studies of this platform. Through the collection of all tweets containing a set of hashtags, a researcher is able to construct a dataset of users who are discussing a particular topic. Of course, this comes

Literature Review 71 with its limitations (Bruns & Burgess, 2012; Bruns, Burgess, & Highfield,

2014). Very often, a hashtag-based dataset contains a great deal of noise and spammy messages that need to be dealt with based on the choices of the researcher. The presence of competing hashtags further adds to the complexity of data collection (Bruns & Burgess, 2015). Furthermore, the very decision about which hashtag(s) to collect is itself influenced by the discursive biases of the researcher, and their familiarity with the communication context of the study.

There is also the issue of Twitter’s control over how much, and what data one can collect from its API (John & Nissenbaum, 2018); and, in case of highly discussed topics, the data carries the risk of being rate-limited. Twitter’s free- to-use APIs allow for the collection of 1% of all tweets posted on the platform at any given time, or approximately a week’s worth of tweets if one is interested in historical data. Therefore, if more than 1% of tweets on the platform contain a specific hashtag while data is beingcollected, the researcher cannot collect anything exceeding this rate.

There is also the limitation of how Twitter users actually tweet. While I could not find literature that shows conclusive evidence regarding the percentage of all tweets that contain hashtags at any given time, evidence from the ongoing collection of Australian tweets by TrISMA (Bruns et al., 2016) shows that it generally does not exceed 20%. In other words, at any given time, the majority of tweets posted on the platform do not contain any hashtags; meanwhile, it is quite likely that many of the non-hashtagged tweets are, in fact, discussing the very issue a researcher is interested in.

Another approach to studying communication on Twitter that is often combined with hashtag queries, is to include keywords and/or phrases in the

72 Literature Review query (for examples, see: Barberá, Jost, Nagler, Tucker, & Bonneau, 2015;

Hilbert et al., 2016; Himelboim et al., 2013; Russell Neuman, Guggenheim, Mo

Jang, & Bae, 2014). While this approach accounts for some of the limitations in hashtag-only queries, it still carries other limitations, such as the risk of being rate-limited, or of missing/excluding other keywords and phrases.

Finally, some studies focus on the users themselves, rather than hashtags and keywords. This is particularly useful if a study is interested in the communication practices of certain users, or the engagement of other users with particular entities such as politicians (Choi, Sang, & Woo Park, 2014), journalists (Hanusch & Bruns, 2017), and organisations. Again, while this approach is particularly useful for certain lines of enquiry, it also faces certain limitations. Collecting tweets with a particular user’s Twitter handle, for instance, does not necessarily guarantee that all tweets discussing that user are collected. A common practice on Twitter, sometimes referred to as ‘sub- tweeting’, involves users talking about others without using their Twitter handle, in order not to be seen by them. Additionally, sometimes users screenshot tweets by others, and share them without using their Twitter handle. Since sub-tweeting breaks the norms of visibility practices on Twitter

(e.g. using the @username to mention other accounts), this practice—often intentionally used to make such tweets less discoverable—creates limitations for any study that collects tweets by using conventional data collection and analysis strategies. In terms of studying networks of interactions, therefore, analyses done using the @usernames of accounts on Twitter generally miss such interactions.

Finally, and especially for studies that focus on activism and collective practices, it is also likely that certain users, particularly the ‘crowd-sourced

Literature Review 73 elites’ (Papacharissi, 2015) might be excluded, simply because they have not been under the radar of the researcher, who might be focusing on well-known, already established opinion leaders.

Briefly put, any approach to collecting data from, and studying communication on Twitter comes with certain limitations, ethical choices, inherent biases, and so on (Stieglitz, Mirbabaie, Ross, & Neuberger, 2018). This is particularly one of the reasons why a study of Twitter communication cannot be generalised to be representative of all communication on the platform, let alone in a whole society. Even if quantitative, statistical measures are used, their value is primarily relational, in that they allow for comparisons between the level of visibility, activity, engagement, and so on. However, these metrics should not be treated as generalisable to broader structures, practices, and the like.

In the present study, while I have used quantitative metrics alongside qualitative approaches, the value of quantitative measures lies in their relational aspect. For instance, while I provide quantitative metrics regarding the number of hashtags, retweets, @mentions, shared URLs, and so on, this does not mean that I have collected all the possible alternatives; rather, it shows that in the collected dataset, and under equal conditions for all other variables used in the collection of data, a particular practice is seen more or less than others. The value of these quantitative measures, therefore, is in their comparability: I do not treat them as objective indicators for generalisable truths. In short, quantitative measures in this study provide empirical evidence, but not objective truths.

74 Literature Review 3.7 The Australian Twittersphere

In the previous sections, I pointed out some of the inherent limitations of the different approaches to studying communication on Twitter. Apart from the limitations with regard to the data itself—such as the risk of missing some hashtags, keywords, or users—another issue with such data collection approaches is the lack of a standard baseline against which communication patterns can be compared; that is, a hashtag collection, for instance, can provide the researcher with a theoretically complete dataset of all the tweets with that particular hashtag. However, if the researcher is interested in knowing what cross-sections of the Twitter population have contributed to the dataset, or what communities of users/ideologies/discourses are involved in the discussion, a hashtag collection cannot provide the complete picture (Bruns et al., 2014; Bruns, Moon, Münch, & Sadkowsky, 2017).

To address some of the aforementioned limitations, and to allow for large-scale, historical, and context-aware big data analyses, Bruns et al. iteratively identified Australian Twitter users. Tweets posted by these users are collected on an ongoing basis in the TrISMA project (Bruns et al., 2016). Furthermore, this collection allows for the analysis of a national Twittersphere, which acts as the basis for the present project.

The first iteration of the Australian Twittersphere used a snowballing approach, crawling the Twitter platform to identify accounts that could be identified as Australian. Given the unique time zones in Australia, Bruns et al.

(2014) were able to use the time zones, profile descriptions, and location information of accounts as criteria to identify Australian Twitter accounts, and then snowball from these to collect follower-followee networks. The project started from a seed list of accounts that were participating in particularly

Literature Review 75 Australian topics such as #AusVotes (for the 2010 Australian elections). This list was then used to crawl further, investigating the followers and followees of the identified accounts. Bruns et al. (2014) then visualised the follower-followee network of the best-connected 120,000 Australian Twitter accounts, and did a qualitative close reading of the profiles of the core accounts in each of the identified clusters in the network.

Bruns et al.’s (2014) findings suggest the presence of ‘thematic clusters’ in the

Australian Twittersphere, where users with similar interests form dense communities of follower–followee relationships. Furthermore, this study, which was one of the few comprehensive studies of a national Twittersphere at the time, also shows that unlike other platforms such as Facebook, communities of users on Twitter are “based largely on thematic affinity, rather than on family relationships or personal friendships” (Bruns et al., 2014; emphasis mine).

Finally, another important finding of the study is its argument against the presence of separate interest groups who are not exposed to information from their antagonist. Rather, although there are distinct communities of affinity- based users, these communities have considerable interconnections, and there are substantial overlaps between them.

Further expanding this initial collection, yet with a different approach, Bruns et al. (2017) continued the iterative snowballing process, adding more accounts to the network of the follower–followee relationships. In this second iteration, they used the unique numerical IDs that Twitter assigns to each account on the platform, to crawl the whole global userbase of the platform, starting from an ID of 1 and stopping at 4,930,000,000, once no further accounts were identified. This list of accounts in Twitter’s global dataset was then crawled based on the criteria of ‘Australian-ness’, and yielded approximately 4 million

76 Literature Review Australian Twitter accounts. Using network analysis and visualisation software

Gephi (Bastian, Heymann, & Jacomy, 2009), Bruns et al. (2017) then created a network map of the follower–followee relationships among the 250,000 globally best-connected accounts in this dataset. Implementing community detection algorithms, they identified a number of clusters in this network, and qualitatively examined the top 100 accounts in each cluster to discover the interests and discourses shared by the group of accounts. They repeated the community detection process at a more fine-grained resolution, which potentially identifies sub-clusters within the larger communities. Within the

‘sports’ cluster, for instance, they identified sub-clusters of users interested in different sports, such as football (Australian Rules), rugby, horse-racing.

By comparing the network of the Australian Twittersphere in their first (Bruns et al., 2014) and second iterations (Bruns et al., 2017), Bruns et al. show that the structures of the Australian Twittersphere have largely remained the same since their early formation. That is, follower–followee networks generally remain structurally stable over time. With regard to the level of activity in each cluster, their analysis points to a higher level of activity in politically- engaged clusters, especially the ‘Hard Right Politics’ cluster, with an average of 12.51 tweets per day, per user, over the lifetime of an account.

Bruns et al. (2017) point to some limitations in their focus on the best- connected accounts in the network, rather than its entirety. Particularly, they note that accounts with a lower number of connections might be “more random in their following choices” (ibid. p. 13).

Literature Review 77

Figure 3-2: The Australian Twittersphere in 2016 (Bruns et al., 2017) In his study of the Australian Twittersphere, Münch (2019) also raises concerns about the potential limitations of focusing only on the best-connected accounts, rather than the whole network. His study also focuses on whether there are any biases transferred to the visualisation due to the particular community detection algorithm that Bruns et al. (2017) use. Münch points to the fact that because the community detection algorithm used in their study necessarily focuses on the density of existing links between accounts, it potentially finds

“echo chambers” (Münch, 2019). Additionally, he posits that a mere focus on the number of links in detecting communities could likely lead to apophenia; that is, to the detection of communities that do not necessarily share discourses, but happen to have the same number of links among them. To verify whether these potential biases and false positives actually exist in the map of the Australian Twittersphere (Bruns et al., 2017), and to investigate the network of all the four million accounts—rather than only the best- connected ones—he used two different community detection algorithms, and a keyword analysis of tweets posted by each community he identified.

78 Literature Review Münch’s study points to a number of conclusions. First, he finds that the overall structure of the network does not change drastically, even if all the accounts are included in the network. Furthermore, his study confirms Bruns et al.’s findings that the identified clusters are, in fact, affinity-based interest groups of users, regardless of the community detection algorithms used to identify them. Finally, his keyword analysis of the tweets posted by the different communities—rather than profile descriptions of the top accounts— also shows that each cluster does, in fact, focus on a limited range of issues and topics, based on their discourses.

What is identified as a cluster of follower–followee relationships in the map of the Australian Twittersphere, therefore, is in practice a ‘community’ of discourse. I do not, of course, use ‘community’ in its traditional sense. Rather, the notion of community here is much closer to the concept of assemblage. The identified communities in the Australian Twittersphere are collectives of accounts, users, materialities, and discourses. Therefore, although I sometimes use the phrases ‘discourse community’, ‘assemblage’, ‘collectives’, and ‘clusters’ interchangeably, they all necessarily mean the same in this context.

My choice to use one phrase over another in different places is based on whether it sounds aesthetically pleasing in a particular sentence, or in some cases, on the need to avoid conflation of a term with its usage in other disciplines or lines of research. The choice to use intra-, inter- and extra- ‘cluster’, for instance, was mostly due to the latter. Using ‘community’ in this sense, for instance, would privilege ‘users’ and agency over the other dimensions of the discursive-material knot. Additionally, the term ‘community’ could potentially mask the limits of what constitutes a discursive-material assemblage on

Twitter, in that it would not emphasise the entanglement of the horizontal and

Literature Review 79 vertical contexts as much as the term ‘cluster’ does (Section 3.5). Finally, my usage of the term ‘community’ is cognisant of the fact that the communities in the Australian Twittersphere are not necessarily generalisable to their counterpart communities in the Australian socio-political sphere. Not every

Australian is on Twitter, and not all topics, issues, and discourses find their way to the Australian Twittersphere.

In the same way, the use of ‘discourse’ or ‘discursive’ would privilege discourse over the other dimensions of the knot. Furthermore, it would also mean that the term ‘extra-discursive’ would lead to confusion, given that within the theoretical framework of the study, ‘extra-discursive’ would be conflated with the ‘constitutive outside’ of the discursive formations; even worse, it would be confused with the distinction between ‘discursive’ and ‘non-discursive’, which

I do not favour. Briefly put, therefore, by using the term ‘cluster’ in this thesis,

I both encompass all the dimensions of the discursive-material knot, coming together under an assemblage or cluster, and employ a term that is more in line with social network analysis.

With this in mind, the clusters in the map of the Australian Twittersphere find their discourse-theoretical position in this research. What is identified as a dense cluster of accounts that follow each other more than they do others in this map is, therefore, an assemblage of privileged discourses; of the material affordances of Twitter; of the agency of the users in choosing who/what to follow; and of the structural dimensions of both society and the platform’s technological infrastructure.

80 Literature Review 3.8 Some Limitations

Using social network analysis in general, and community detection algorithms in particular, has its own limitations, which need to be acknowledged here.

Especially important for the present study, one of the limitations of the map of the Australian Twittersphere with regard to theories of discourse is that, theoretically, an individual can belong to a number of different discourses and discourse communities at the same time. This ‘split’ identity is what actually allows for discursive struggles (Laclau & Mouffe, 1985, p. 63). Personally, as a social scientist using digital methods to investigate Twitter, I am academically a member of several discourses at the same time (e.g. social sciences, digital methods, platform studies, and Twitter scholar discourses). At the same time,

I have musical preferences, hobbies, and the like, each of which puts me in a different discourse.

This mapping of the discourses I belong to, and operate in could be endless.

However, the limitation of using a community detection algorithm to visualise my membership in a discourse lies in the fact that my material manifestation on Twitter only presents itself as one node in a network. This potentially positions me in the network based on the number of accounts I follow and/or am followed by, or on some other criterion, depending on the algorithm used.

In this case, if I follow more musicians on Twitter, I am more likely to be positioned in the music cluster. This limitation, of course, could be circumvented using other community detection algorithms; however, it is neither an objective of this study, nor in my range of skills and abilities to do so (at least for now).

The aforementioned limitation, therefore, is important to consider in this project in that it essentially creates binary oppositions, especially in the case

Literature Review 81 of network clusters belonging to the domain of politics. A user’s position in a cluster is merely an indication of the number of other users in their network who follow similar users. However, in some cases, this positioning can lead to false positives. Take, for instance, a political journalist whose job requires them to follow a wide range of accounts to be aware of the political discussions on

Twitter. If, for some reason, the journalist follows more right-wing accounts than progressive ones, they will be positioned in the right-wing cluster in the map of the Australian Twittersphere. Although, at aggregate levels, this does not skew the findings of the study, it is a limitation that needs to be considered.

This is also one of the reasons why I argue (Section 3.6.1) that the value of quantitative and statistical measures in this study is in their comparative and relational findings: They should not be treated as measures of objective truths about the communication environment.

Another limitation of using the Australian Twittersphere as a baseline for some of the quantitative analyses I do in this study is that I use the latest iteration of the network of the follower–followee relationships in the Australian

Twittersphere. This iteration includes approximately 250,000 accounts, and is based on data from 2016. This means that accounts created after 2016, or accounts that are less connected, will at times be excluded from my quantitative analyses (in Chapters 6 and 7). In instances where I use the map of the Australian Twittersphere as a foundation for cross-referencing the accounts in the different identified usters—especiallycl when calculating the levels of intra- and inter-cluster interactions (Sections 7.3 and 7.5)—my analysis is limited to only the accounts in the network of the follower–followee relationship among the best-connected accounts in the Australian

Twittersphere. This creates some limitations on the generalisability of the findings to the whole Australian Twittersphere. Again, this does not necessarily

82 Literature Review pose a threat to the findings of the study, but is still a limitation that I am aware of. To address some aspects of this limitation—at least partially—I opt for an iterative, mixed-methods approach. This approach constantly moves back and forth between quantitative analyses of the whole sample, cross- referencing it with the network of the Australian Twittersphere, and qualitative analyses of the texts of the tweets. In this way, I triangulate the findings that are limited to the best-connected accounts in the map of the

Australian Twittersphere with the findings of the other stages of the study.

Finally, something to note regarding the labelling of discourses and clusters in this study is that I generally remain loyal to the original labels assigned to the clusters in the Australian Twittersphere. However, given that this labelling is based on a close reading of the Twitter profiles of the core accounts in each cluster in the network, it can have its own biases and limitations. In the case of the clusters at the left and right of the political spectrum, for instance, this categorisation essentially lumps a range of discourses together. The

‘Progressive Politics’ cluster, for instance, could potentially include a range of similar discourses (e.g. feminism, human rights advocacy, Marxism, socialism, social progressivism, political progressivism, liberalism, or anarchism). The same is true for the ‘Hard-Right Politics’ cluster, which potentially groups a range of similar discourses (such as libertarianism, free market advocacy, ultra- conservatism, alt-right, and ethno-nationalism).

Although I frequently use these labels to refer to the identified clusters in the

Australian Twittersphere and in my own study, this should not be understood as an essentialist and reductionist approach. I have been reflexive and careful throughout not to generalise the findings of the study to all the discourses within the ranges encompassed by the labels; nevertheless, it remains a

Literature Review 83 limitation of this study. In one case, as I discuss later (in Section 7.3.2), my findings do not perfectly fit the label used for the cluster of ‘Progressive

Political Commentators’ in the Australian Twittersphere. Rather, they point to a hard-left discourse. Other than this particular case, the range of discourses in each cluster has enough similarities to be in agreement with the general labels used in the original study of the Australian Twittersphere.

It is also worth emphasising here that the labels used for the different clusters in the Australian Twittersphere have been assigned to them based on a qualitative close reading and interpretation of the Twitter profiles of the core users, and the keywords in their tweets. My findings, and those of Bruns et al.

(2014, 2017) and Münch (2019), concur with regard to the choice of labels; nevertheless, it is important to note that these labels are simplified interpretations of the data, and are not completely bias-free. That is, the labels themselves reflect the contingent nature of our discourses, and can have fluid and disputable readings. What one might consider to be an indication of a progressive or hard-right discourse might be different than another’s interpretation.

At a broader scale, and perhaps applicable to any study of Twitter, is the inherent limitation that Twitter is generally not the most popular, or the main social media platform in many societies, including Australia. According to

Sensis’s survey (Sensis, 2018), only around 20% of Australian social media users are active users of Twitter. Therefore, the findings of a study on the Australian

Twittersphere are not representative of the whole Australian society.

84 Literature Review 4 Research Design

4.1 Introduction

In the previous chapters, I discussed the overall theoretical framework of the study, and argued for methodologies that that are cognisant of, and can account for the complexities of studying social media platforms: their often- large volumes of data, their materialities, and the discursivity of actions on them.

Building on this argument, this chapter details the methodological procedure used in the three case studies that form the research project. The main focus of this chapter is on the processes involved in the data collection and analysis, and the steps involved in each phase. In each section, I briefly discuss how these metrics, tools, and methods connect to the theoretical framework of the study. However, these connections between high theory and methodological procedures are examined in more detail in Chapters 6, 7, and 8. That is, drawing from the discourse-theoretical analysis, in this chapter I mainly focus on the tertiary sensitising concepts of the study (Section 2.3.2)—concepts external to the discursive-material theoretical framework, but directly relevant to a study of social media platforms, particularly Twitter.

I finish the chapter with notes on the ethical considerations and choices that inform the execution and presentation of this research.

4.2 Data Collection and Preparation

The initial data collection for each case study in this project began with exploring the contextual environment within which the case at hand arose

Research Design 85 (including a review of the historical evolution of the debate); the news related to the issue; and the relevant legal documents and laws pertaining to the study.

This process of familiarising myself with the case study then guided me in making a list of key terms, phrases, names, hashtags, and the like, which are central in the discussions of the issue, and frequently seen in the debate. This initial list served as the first step in the data collection process.

This project benefitted from a multi-university infrastructure designed for studying social media platforms in Australia. TrISMA—the Tracking

Infrastructure for Social Media Analysis (Bruns et al., 2016)—has identified approximately four million Australian Twitter users6. Tweets posted by these accounts are collected on an ongoing basis and stored in TrISMA’s databases.

Collecting tweets on an ongoing basis also means that when a researcher notices that they might need to add additional keywords or phrases to their data collection, they can do so immediately. Furthermore, TrISMA also allowed me to limit the scope of the study to Australian Twitter users only, if required.

This particular approach enabled this study to circumvent a number of limitations that often impact studies of Twitter. Using Twitter’s free APIs

(Application Programming Interface), for instance, a researcher is often restricted to only collecting data from the moment a collector is set up.

Regarding historical data, Twitter’s APIs only allow for collection of tweets from about the past week. Such limitations make it practically impossible for

6 For the first two case studies, I use this database of four-million accounts. However, changes imposed by Twitter to its APIs forced TrISMA developers to adapt by limiting the collection to the most active 500,000 accounts in the Australian Twittersphere. My third case study used this new collection. While this did not negatively impact this project, it is still a limitation of this study. Also, the last iteration of TrISMA to identify Australian Twitter users was in 2016. Therefore, my datasets do not contain tweets by Australian Twitter accounts created after 2016.

86 Research Design a project to use Twitter’s free APIs to collect historical data. The approach used in TrISMA, however, made it possible for this study to collect historical data.

Taking advantage of the opportunities provided by TrISMA, I started the data collection from the context-aware list of terms, phrases, keywords, names and hashtags by querying TrISMA databases for any tweets containing one or more of them, posted in the timeframes under investigation. At this stage, however, the collected dataset could not be considered as final. Rather, I treated it as the first window to theanalytical process.

From this stage onwards, a number of considerations guided each step of this process. First, given that the initial list used in data collection sometimes contained acronyms, common names, or words that might have different meanings in different contexts, it is possible that the initial dataset could have some level of ‘noise’. In such cases, I removed these tweets upon discovery.

Additionally, the findings of the different steps in the analytical procedure also helped to modify the initial dataset. If I encountered new and relevant keywords and phrases that were not included in the initial query, I added them to the dataset. This process not only led to a more representative dataset, but also addressed possible biases in the initial list used in the data collection.

Finally, with regard to the oft-discussed issue of ‘bots’ on Twitter, the analytical procedure and in-depth examination of the tweets in the dataset pointed at times to the presence of bots, and automated and/or spam accounts.

On facing such circumstances, where an account was clearly a ‘spammy’ one,

I removed it from the dataset. These included, for instance, accounts that only tweeted several irrelevant hashtags together in the text of their tweets, with no text accompanying them. Also, there are accounts in the datasets that

Research Design 87 tweeted a limited number of sentences at a very regular interval several times an hour, thus pointing to their automated tweeting of pre-defined sentences.

However, I did not use a particular tool or algorithm to detect all possible bots in the datasets, so it is still possible that there is some bot activity in them. I made the choice not to remove all bots because, under the theoretical framework of this study, such accounts can still influence the communication environment, and are, therefore, parts of the discursive struggle in the discursive-material knot. Simply put, if a bot is ‘smart’ enough not to be detected in the analytical procedure, I treat it as yet another account, with a potential to influence the communications and discursive struggles on the platform.

The data collection stage in this study, therefore, was not an isolated step that ended before the analysis started; rather, it was an iterative process that self- repeated with each step of the study, continuously being modified in a snowballing fashion.

4.3 Analytical Procedure

4.3.1 Phase 1: Temporal and Aggregate Metrics of Discourse

For each case study, I first started with an investigation of tweeting patterns over time. This involved looking for rises and falls in the number of tweets over the period under investigation. Peaks in the number of tweets posted at different times can be understood as indicative of Twitter users’ increased interest in an issue. The investigation of tweets over time also involved an

88 Research Design examination of the types of tweets7, and the discourse communities from which they originated. A comparison of the peaks and troughs in tweeting patterns over the evolution of the debate in the socio-political context— such as the unfolding of events, and the publication of news—pointed this study to the various discourses involved in the debates, and to how Twitter users react to events in society. This becomes more significant in cases where different discourse communities exhibit divergent tweeting patterns (e.g. when one discourse community becomes much more active, while another tweets less actively). Such divergences reveal important clues about the discursive strategies involved in the representations of issues (an example of these is discussed in Section 6.2.2).

The second affordance significant for this study is the temporal and aggregate use of primary and secondary hashtags. Hashtags, as one of the key affordances of Twitter, contain valuable information about the discourses involved in a debate. Very often, hashtags are used as topic markers, affective clues, discourse tags, and/or interdiscursive references (Giglietto & Lee, 2017;

Zappavigna, 2011). Therefore, an examination of the hashtags collectively used by the actors and communities involved in a debate provided this study with valuable information about the discursive positions and strategies used by different communities in framing, representing, and channelling the debate.

In this stage of the exploratory phase, I was particularly interested in the different hashtags used by the variouscommunities involved in the debate, and the functions these hashtags performed. Especially important for this study, was the use of hashtags as interdiscursive articulations, where users

7 Tweets are classified as ‘retweet’, ‘@mention’, and ‘original’ in the TrISMA databases. In cases where a retweet also contains an @mention of other accounts, it is classified as both a retweet and an @mention.

Research Design 89 strategically connected the discourse under investigation to other discourses and re-contextualised semiotic elements. Salience of certain interdiscursive references in the discourse of one community, along with an absence of references to other discourses, provides insights into the symbolic resources and discursive formations available to the various communities in the debates.

Additionally, given that I started the data collection from a pre-defined list of keywords, phrases, and hashtags, an examination of the most frequently used hashtags in the first stages of the data analysis also revealed any other potentially significant hashtags that I might have missed. In such cases, the initially collected dataset was supplemented by the newly discovered hashtags before moving to the next stages of the analysis.

As a next step, I examined the hyperlinks shared by the different actors and communities, in order to further discern the information sources and discourses involved in the debate. Given the character limits of a tweet (140 characters until mid-2017, and 280 afterwards), URLs have become an important tool for sharing links to various information sources within a tweet. In this regard, two central issues were important for this study. First, the collective sharing of links to external sources is a matter of attention for this research, in that it is an indicator of the information sources shared by users; that is, I am particularly interested in the question of what is perceived worthwhile for users to share on Twitter. Second, given that a multitude of different discourses, actors, and organisations were involved in each of the case studies, the question of who shares what was also of significance.

Previous literature shows the tendency of individuals to engage more with the information that is in line with their previously held beliefs, discourses, and ideologies (Colleoni, Rozza, & Arvidsson, 2014; Garrett, 2009). This tendency,

90 Research Design which is theorised as selective exposure and/or homophily, can potentially result in the formation of patterns in certain communities sharing certain URLs and avoiding others. As a result, the answer to the question who shares what is significant in the examination of information sources shared by different discourse communities.

In the final steps of the exploratory phase, I moved my attention away from the metrics that can be conceptualised as media objects (e.g. hashtags, URLs, and tweets), and focused instead on the metrics directly related to the human objects. Of course, I do not mean ‘human’ in the literal sense of the term, since any Twitter account is, in essence, only a mediated avatar. However, these digitally mediated avatars, even in cases when they are automated accounts and bots, are eventually tied to a human agency; this agency, in turn, is situated within a discourse and, at the same time, is constrained by the limits of the platform on which it operates. In this sense, the term ‘human object’ here refers to the knotted collection of structure, agency, discourse, and the materialities of Twitter, all coming together under a Twitter username. This username (or profile, handle, account), then, is itself an actant that tweets and is retweeted; shares information; and follows and is followed by other actants.

Twitter handles, therefore, follow both the logics of the structures and discourses within which they are situated and, at the same time, the logics of the platform in which they operate. With regard to the logics of Twitter as the platform, a number of metrics are more relevant for this study. These include the follower counts, the level of activity, and the level of visibility. The last steps in the exploratory phase focused on these metrics.

The level of activity of an account, such as the number of tweets posted, or how much the account engages with others through retweeting or @mentioning

Research Design 91 them, can potentially influence the visibility of the given account; this visibility, in turn, can lead to the account’s accumulation of discursive power.

Identification of the most active accounts in the dataset, therefore, provided a window into the dynamics of visibility and discursive power. However, the level of activity alone cannot be considered as the only indication of discursive power. It is possible that a highly active account does not have the number of followers that can give its messages a critical mass to be read by many others.

Also, it is possible that an account with many followers does not have the

‘right’ followers. Such can be the case when an account is followed by a large number of users who do not follow many other accounts in the broader discursive environment. Examples of this can be seen in the ‘Teen Culture’ cluster in the Australian Twittersphere. Bruns et al.’s (2017) study of the network map of the Australian Twittersphere shows that this large cluster is generally more isolated from the rest of the network. Although some accounts, such as those of celebrities, have a large following in this cluster, the isolated nature of this cluster makes messages originating from it to be more likely to be distributed within that community only, and not reach the broader

Australian Twittersphere. As a result, I did not necessarily consider the level of activity and follower numbers as separate analytical entities; rather, I moved back and forth between the two when investigating the potential visibility and discursive power of accounts in the datasets.

Another issue which further complicates the logics of visibility on social media platforms, and Twitter specifically, is direct or indirect endorsement of messages posted by an account. These endorsements, which can be seen in the number of retweets received by an account, also point to the given account’s level of potential visibility: The more that tweets posted by an account are retweeted, the more chance the account and its tweets have of being seen and

92 Research Design read, and accumulating discursive power. The number of retweets received by different accounts, therefore, is another metric that I considered with regard to the metrics of potential visibility.

Finally, visibility should not be seen in a monolithic fashion. In fact, and at least on Twitter, the kind of visibility of an account is also important. An account could be visible due to its socio-political status, such as Twitter accounts of politicians, journalists, pundits, and celebrities. Also, an account can be visible due to its controversial or contested role, or because other accounts are attempting to put pressure on it to voice an opinion or react to an issue through giving it visibility. Such scenarios can be seen in the level of

@mentions an account receives, and the kind of information directed at them.

The politics of @mentioning on Twitter, and the functionalities of an

@mention, afford users the possibility of giving visibility to other accounts through means other than endorsement or retweeting. Therefore, I also looked for the most @mentioned accounts in a discourse to examine who was being talked at/about/to, and to further investigate why these accounts were

@mentioned most.

Of course, it is worth emphasising again that these account-related metrics and visibility indicators cannot be seen as being in a vacuum, separate from each other. Although I present them in a separate fashion, dedicating a paragraph to each, this does not mean that the analysis itself investigated them as separate entities. Rather, when investigating patterns of visibility and discursive power, the aforementioned metrics were always simultaneously and recursively reviewed and examined.

Research Design 93 4.3.2 Phase 2: Network Metrics of Articulation

The first phase of data collection andanalysis provided insights into the broader discursive environment in which the debates, discussions, and conversations took place. Identification of patterns of tweeting activities, the collective uses of hashtags, and the characteristics of the actors involved in the debate, aided the formation of a series of questions and points of interest for a deeper investigation. While metrics such as temporal patterns, visibility, information sources, and hashtags provided useful details and insights into the collective patterns of activity, it was also crucial to consider the networked nature of articulations that were formed as a result of these collective tweeting patterns by, and among, Twitter users. The next phase in the data analysis, then, involved looking for patterns in which Twitter users articulated and reinforced discourses, engaged with competing discourses, and amplified their voice in order to accumulate discursive power and assume a hegemonic position in the discursive struggles.

This stage of analysis followed a simple assumption: If User A tweets something, and User B reads the tweet, User B is faced with a number of possible choices: to either not engage with the tweet (which is itself a discursive strategy), or to engage with it in some form or another. User B might decide to circulate the message through retweeting, or to engage in an attempted conversation with User A through an @reply. If User B decides to engage with the tweet in any form, this, in turn, circulates the tweet to User B’s followers, who then face similar choices. This cascade of engagement can theoretically continue until no further users circulate the message. As a result, if a group of users pass on messages from another user or community through retweeting or

94 Research Design @mentioning, the end-result is the formation of a cluster of interconnected users.

This phenomenon can be investigated using techniques and tools developed in the area of science commonly known as Social Network Analysis (SNA). Under the same assumption, if the hypothetical community of users discussed above makes a choice not to engage with tweets from other users or communities, this creates separate and polarised clusters of Twitter users who engage with some, but not with others. These dynamics of articulatory relations can reveal crucial information about how users and communities engage among themselves and with others. I conceptualise these relations with the terms intra-cluster and inter-cluster dynamics in Section 1.2.

The same logic applies to other forms of relations and articulations allowed by the affordances of Twitter. However, due to the different functionalities of affordances, the structures of the formed networks and their dynamics expectedly reveal different types of information about the discursive struggles involved in each case study. I discuss these different dynamics, and their discourse-theoretical implications, in Chapter 7.

In this stage, therefore, a number of different network formations were relevant to this study. I started with an investigation of the network of retweets, through processing the dataset to find any account that had retweeted another.

This directional network, therefore, consisted of the accounts that posted a tweet as the target; the accounts which retweeted the given post as the sources; and each retweet instance as an edge between the two accounts. I then analysed and visualised this network with the open source network analysis and visualisation software Gephi (Bastian et al., 2009), using the Force Atlas 2 algorithm (Jacomy, Venturini, Heymann, & Bastian, 2014). This step revealed

Research Design 95 the overall structure of the network of retweets, and whether it took a core– periphery, polarised, multipolar, or any other form. These different forms of network structure reveal different conversational dynamics (M. A. Smith,

Rainie, Himelboim, & Shneiderman, 2014).

Having identified any possible clustering tendencies in the network, I then moved to identify the core accounts in each of these clusters (the accounts with the highest weighted indegree). To make inferences and assumptions about the discourses and political orientations of the identified clusters, I qualitatively examined their Twitter profiles, their tweets in the dataset, and contextual information about them in cases where they were well-known public figures. In the close reading of Twitter profiles of the accounts and their tweets, I focused on explicit markers of the political leanings of the account, including their profile description (e.g. whether theyself-described as conservative, ‘lefty’,

Labor supporter, or activist); their stance on each case study issue (e.g. whether they supported changes to Section 18C, or called for closure of offshore detention camps); the partisan hashtags they used (e.g. #StopTheBoats,

#BringThemHere, #Repeal18C); and, in some cases, their Twitter profile pictures (e.g. when the user’s profile picture had an explicit political message, and was not simply a photo of the account owner).

I repeated the same process for the network of @mentions, where the user posting a tweet containing an @mention of another account is treated as the source; the user receiving the @mention is treated as the target; and any instance of @mention as an edge between the two. The difference between the retweet and @mention network lies in the functionalities and discursive strategies afforded by them. While clusters in the retweet network can potentially reveal the discourse of the communities, this is not necessarily the

96 Research Design case for the @mentions network. Rather, the clusters formed in the @mention network might indicate the perceived discursive positions of the accounts to which @mentions are directed. As a hypothetical case in point, if a large number of users perceive an issue to be related to a particular group of users

(e.g. environmental activists, a certain political party, or a group of organisations), the given group of users are likely to @mention the accounts they perceive as relevant more than they do other accounts. This, in turn, leads to the formation of clusters in the @mention network, in which the group of accounts perceived as pertinent to the case are @mentioned together in a large number of tweets by a large number of users. This can lead to two possible scenarios: 1) If the targets are @mentioned by a large group of users, they are placed in the centre of the network (e.g. if a majority of users @mention a number of politicians); or 2) If a group of accounts @mention each other extensively, but do not @mention or are not widely @mentioned by other accounts in the network, they form clusters at the peripheries of the network, and not at the centre. Such differences in the structure of @mention networks, therefore, can reveal crucial information about the perceived role of different accounts in the discursive environment.

Apart from the two most common engagement affordances of Twitter— retweets and @mentions—other articulatory practices can also form network structures that can unravel the dynamics of meaning making and discursive struggles. Moving on from the networks of retweets and @mentions, I then processed the dataset for structures and patterns formed as a result of the collective uses of hashtags and URLs.

The network of accounts and hashtags, in which an edge is formed between an account and each hashtag they have used in the dataset, reveals information

Research Design 97 about the use of hashtags as discursive strategies. Again, the working principle here follows the same assumptions in the networks of retweets and @mentions:

If a group of accounts frequently use certain hashtags, but not others, clusters will form; qualitative reading of these hashtags, then, reveals information about the discursive positions and symbolic resources shared by that community of users.

I also followed the same principle in my investigation of the account–URL network, where an edge is formed between an account and each domain they have used in the dataset. Expectedly, if a group of accounts repeatedly share

URLs from the same sources, and avoid sharing links to others, clusters will form. These clusters indicate the information sources, potential biases, and discursive positions of the given community of users.

4.3.2.1 From Ad-Hoc Articulations to Sedimented Discourses

The various network structures discussed in the previous section provided this study with valuable information about the formation of discursive communities, interactions between these communities, and the dynamics of discursive struggles in each case study. However, two questions remained unanswered by the tools and techniques used so far. First, each of the different aforementioned networks is an indicator of a transient, issue-based, ad-hoc public (Bruns & Burgess, 2015), within which Twitter users discuss a certain issue, engage in potential conversations and debate, and amplify their discourses. However, the question that remained unexamined here was the extent to which these temporary, ad-hoc discursive positions and communities follow longer-standing and more sedimented discursive formations in the

Australian Twittersphere. In other words, do all the users retweeting (ultra-) conservative tweets about a certain case belong to the discourse community of

98 Research Design (ultra-) conservatives in the Australian Twittersphere? Is it possible that a user that is normally in the far-right of the political spectrum holds more socially progressive views about an issue?

Second, the other question worth inspecting in this regard is the extent to which antagonistic discourse communities in the Australian Twittersphere are exposed to the discourse of the Other. That is, the question is whether the affordances of Twitter facilitate the discursive struggles, and whether any form of agonism can form as a result of the affordances ofTwitter. Or, on the contrary, if the material design of Twitter, as a platform, is formed in a way that prevents any inter-cluster interactions, potentially inhibiting any possibilities for an agonistic space and instead amplifying antagonisms.

To address these questions, the last step in the study of the network structures in the dataset examined these temporary, issue-based structures within their longer-term discursive formations and communities. I began by overlaying the network of retweets and @mentions on the network map of the Australian

Twittersphere, a network representation of the follower/followee relationships among the approximately 250,000 best globally connected Australian Twitter users (Bruns et al., 2017). As discussed in Chapter 3 (Section 3.7), the clusters in this network represent the various discourse communities in the Australian

Twittersphere, and since follower/followee relationships are generally more permanent than retweeting and/or @mentioning patterns, a comparison of the structures of the retweet and @mention networks with this map provided answers regarding the inter-relationship of issue- and affordance-based, ad-hoc network formations on Twitter, and the more permanent, discourse-based follower/followee relationships.

Research Design 99 Finally, with regard to the extent of information exposure and inter-cluster interactions—especially between antagonistic discourse communities—it was important to compare the different levels of interactions that occurred within each community (intra-cluster dynamics) with the level of interaction and information exchange that community had with the others (inter- and extra- cluster dynamics). In order to gain this information, I first compared the number of accounts in each discourse community in the Australian

Twittersphere with the number of accounts in that community that had tweeted about the issue. I took this as an indicator of the level of engagement and participation of that cluster with the case study under investigation. I then selected the most engaged clusters, and compared the levels of intra-, inter-, and extra-cluster interactions. That is, I compared the number of retweets/@mentions among the members of a community with the number of retweets/@mentions among the members of that community and others. I employed Krackhardt’s E–I index (Krackhardt & Stern, 1988) in this regard.

This index provides a normalised measure of whether a given community is more inward- or outward-looking. In other words, if a cluster is more inward- looking in its retweeting patterns, it can be inferred that the accounts in that cluster prefer to only retweet (endorse) tweets originating from their own cluster.

For a hypothetical case where no information travels between two clusters, it can be concluded that the given communities are, in fact, in a filter bubble, where they are hermetically sealed from the discourse of the Other, and no inter-cluster interactions can occur (Bruns, 2017). On the other hand, if the E-

I index indicates that information does travel between (antagonistic) communities, the question worth investigating, then, is what happens to a message once it passes through to the other discourse community. The exposure

100 Research Design of antagonistic communities to the discourse of the Other is the very moment the discursive struggle happens, and a potential for amplification of antagonism or formation of agonism occurs.

The quantitative approach used in this section, however, could not provide all the answers to such questions. Mere information exposure and inter-community interactions do not automatically mean that an agonistic space has been formed. It is also possible that two communities focus on the same topic, make the same arguments, and take the same discursive positions on an issue, but are not necessarily exposed to each other’s discourses due to the design of the platform. This possibility necessitates a qualitative approach to be taken in parallel to the previous stages, to allow the study to also examine the content of the tweets originating from each community. I discuss this in the next section.

The calculation of inter- and intra- cluster dynamics (as detailed above) required further qualification. To makethese comparisons, I relied on the map of the Australian Twittersphere (Bruns et al., 2017), which is based on the

250,000 globally best-connected Twitter accounts in Australia. This meant that

I could only focus on those accounts within this larger network that were in my datasets. As discussed in Section 3.8, there are some inherent limitations in using this approach. Most important to this section of the study is the exclusion of less-connected accounts from the calculations. Precisely because they are less connected, it is possible that such accounts exhibit more random connections to a range of clusters in the Australian Twittersphere. Such random connections, in practice, make it possible for these accounts to act as information bridges between clusters, since they might have followers, or follow accounts from, all the clusters in the discussion.

Research Design 101 This limitation does not change the broader structures in the Australian

Twittersphere to a significant degree (Münch, 2019). However, it might introduce some bias into the interpretation of intra- and inter- cluster dynamics, as my approach in this section only focused on the more engaged, better globally-connected clusters. It is also because of this limitation that the triangulation of findings from this section with the two other phases helped in addressing these potential biases. Since the focus in the social media analytics

(Phase 1) and keyword analysis (Phase 3) is on all the accounts in the datasets, and is not limited to the best globally-connected ones, this limitation could be overcome, at least to some extent.

4.3.3 Phase 3: Textual Articulatory Practices

Having identified the different discourse communities actively involved in the debates in each case study, and the information flows within and among them, it was then necessary to delve more deeply into the symbolic resources, discourses, and discursive strategies employed by these communities. This was necessary to provide a clearer picture of the dynamics of discursive struggles present in the broader discursive environment.

Given the sheer volume of tweets in each case study, and the focus of the project on broader, collective dynamics of discursive struggle rather than the discourse of the elite, the traditional approaches to data selection often used in

(critical) discourse studies could not provide me with the type of information required for this research. As argued by Widdowson (1995), such studies are often more interested in the atypical, out-of-the-ordinary manifestations and representations of a specific discourse. However, the focus of this study is on collective patterns that could be representative of the broader discursive

102 Research Design struggles. Therefore, tools and techniques that can account for large volumes of texts seemed more appropriate for this purpose.

In bridging the gap between the large-scale, quantitative approaches discussed in the previous stages, and the deep-reading, discourse-theoretical perspectives required in this endeavour, corpus linguistics approaches proved useful.

Existing literature shows the appropriateness and suitability of corpus linguistics approaches in discourse analytical studies, especially with regard to online data (Baker et al., 2008; Mautner, 2005). Following the same line of work, the third phase of the methodology started by identifying the most salient themes, topics, symbolic resources, and discursive strategies employed by each of the discourse communities involved in the debates.

Especially relevant to this study, is the development of corpus linguistics techniques, often referred to as ‘keyword analysis’ (Baker, 2004, 2012). The linguistic assumption behind this technique is that there is often a certain statistical and probabilistic distribution of terms and phrases in a series of texts from a specific discourse. A zoological writing about a certain breed of dogs, for instance, is more likely to have frequent references to dogs, that particular breed, and maybe some other breeds of dogs when comparisons are made. This rather intuitive assumption has led to the creation of models and techniques that can identify the statistically unusual presence of certain words and phrases in texts, when compared to a much larger reference corpus from a similar field of discursivity. Building on the example and assumption here, if the term ‘dog’ appears 1000 times in a reference zoology corpus of 10,000,000 words (a probability of 1 per 10,000 words), but only 10 times in a sample corpus of

1000 words (a probability of 1 per 100 words), the word ‘dog’ is identified as a

Research Design 103 keyword in the sample corpus, given its much higher statistical frequency when compared to the reference corpus.

Following the same logic, I used all the tweets in each case study as the reference corpus, and tweets posted by each discourse community as the sample corpus. Using the log-likelihood measure in the open source software AntConc

(Anthony, 2018), I extracted the keywords from each discourse community.

These keywords—which can then be viewed individually in the sentences and tweets they appear in (Keyword in Context or KWIC)—acted as the first window into the qualitative reading of the most representative and salient tweets, themes, topics, and discursive strategies employed by each discourse community.

Having identified the salient features ineach discourse, I then turned my focus onto the discursive strategies and master signifiers in each community. With regard to the questions of intra- and inter- cluster dynamics, the main sensitising concepts in this section were the articulation of one’s own identity, both in relation to maintaining individual and collective identity, and in the antagonistic discursive struggle with the Other. In this regard, I drew from the discursive strategies identified by Reisigl and Wodak (2005, 2016) in their

Discourse-Historical Approach (DHA) to critical discourse studies, which mainly focuses on how discourses represent Us vs. Them dichotomies in different situations.

The five main discursive strategies of identification and antagonism discussed by Reisigl and Wodak (2005, p. 45), which are also pertinent to the study at hand, are the following:

104 Research Design 1. Nomination/referential strategies, looking at how ingroups and

outgroups are constructed and represented, and how they are named

and referred to

2. Attribution/predication strategies, or the attribution of

positive/negative traits to Us and Them

3. Perspectivation/framing strategies, or perspectives and frames used in

the representation of issues, events, and actors

4. Intensification/mitigation strategies, or how different themes, topics,

and discourses are foregrounded or backgrounded, and

5. Argumentation strategies, which investigates how the other strategies

are justified.

This last step, of course is, again, not an isolated step, separate from the findings of the previous stages. Rather, and following the general cyclical approach in qualitative studies, it largely builds on, and triangulates with the findings from the previous stages of the methodology. Additionally, it feeds into those steps, in that the findings from qualitative investigations of the dataset raise new questions and sensitising concepts for the quantitative enquiries.

4.3.3.1 On the Necessity of Methodological Eclecticism

As discussed in the introduction, the end-goal of this project is to provide new insights into the dynamics of discursive struggles in the Australian

Twittersphere through a balanced focus on the myriad forces impacting on the hegemonic processes, antagonism, and agonism on Twitter. A point worthy of emphasis in this regard is that although I draw extensively from both quantitative and qualitative approaches to media studies, this study is neither a quantitative nor a qualitative research project. While the choice of methods

Research Design 105 and tools might seem rather eclectic at times, it is worth noting that they all come together under the epistemological and ontological lenses of Laclau and

Mouffe’s discourse theory (Laclau & Mouffe, 2001)—a theory further developed by Carpentier’s theorisation of the ‘discursive-material knot’ (Carpentier,

2017). In other words, while social media analytics, social network analysis, corpus linguistics, and critical discourse studies are all employed in this research, this study is none of them and all of them at the same time. With regard to network analysis, for instance, it can be argued that I have not fully gone into the details of numerous approaches, algorithms, metrics, and mathematical equations necessary for a study to be properly labelled a ‘network analysis project’. Concurrently, the same can be argued for the critical discourse analysis part of the project. While I draw from the DHA, I have not turned this project into a completely critical discourse analytical study, and have not done the full-scale and thorough textual and linguistic analysis that is characteristic of a DHA study.

While I am aware of such limitations, I also emphasise the role of discourse theory as the apex of the pyramid that builds the different approaches that converge in this project (Section 2.3.1). As Carpentier argues, in such pyramidical conceptualisation of the sensitising concepts that come from the theoretical framework of the study, and also guide the project, “…multi-method and even multi-paradigm research projects remain possible, however difficult it may be to implement these in actual research practice and have them supported by discursive and materialist theories” (Carpentier, 2017, p. 292).

In other words, the eclectic approaches and methodologies used here are justified by their potential to answer the study’s ultimate questions, and they have been systematically converged and drawn from, so as to avoid epistemological contradictions and paradoxes, while serving their purpose in

106 Research Design answering the questions of the project. This, in no way, should be conceived as a case of confirmation bias, where I only select those parts of each approach that can confirm my initial ‘hunches’; quitethe contrary, this is an attempt to triangulate the findings of each phase with the findings of the others, to precisely avoid such biases.

In this sense, all the sides of the methodological pyramid discussed in the previous sections finally come together under the lens of discourse theory, where the findings from each side areinterpreted from the discourse-theoretical perspective (in Chapters 6, 7, and 8). Finally, Chapter 9 presents a synthesised overview of the dynamics of discursive struggle in the Australian

Twittersphere.

4.4 Ethical Considerations

This project is approved by the Office of Research Ethics and Integrity at QUT as a research posing negligible risk to participants and the researcher

(Clearance number 1200000491). This rating broadly covers research in which the risk to participants is no more than “discomfort”. However, I believe that the decision to judge what can be considered a potentially discomforting action is, in itself, an ethical consideration. To make this judgement, I follow the guidelines of the ethics working committee of the Association of Internet

Researchers, which treats the ethical decision-making as a context-dependent and case-dependent process, and not as a universal strict code to abide by

(Markham & Buchanan, 2012).

In balancing the rigour of the research project, its presentation in this thesis, and the privacy and rights of the participants, I was faced with a number of ethical dilemmas. On the one hand, I had the responsibility to present the

Research Design 107 findings of the research in a way that the reader is able to both follow and trust. On the other hand, I had to respect the privacy of the subjects of the study, and present the findings in such a way that they are not identifiable.

This responsibility was further complicated by the fact that (digital) activists play an important role in the project case studies. While I had to maintain their right to privacy, I also had to balance this with their right to visibility.

After all, if an activist dedicates a high level of time and energy to promoting an issue, my actions have to respect their right (and desire) to be seen and heard. This is not an easy task, particularly due to the ahistoricity of online data (KhosraviNik & Unger, 2015).

In the particular context of each case study, users and activists tweeted about each issue. However, my decision to name these users, or to include the texts of their tweets, meant that they would become a permanent part of this thesis.

What if, for some reason, they decide to delete their tweets at a later time, or set their Twitter profiles to ‘private’ mode? What if someone who is permanently ‘on the record’ in this thesis, and who has previously used a pseudonym on Twitter, decides to change their screen name to their real name in the future, having forgotten a problematic tweet they posted years ago. This could potentially now pose a risk to them.

It would have been practically impossible to contact all the users in the dataset and ask for their consent, given that there were thousands of users in each case study. Furthermore, even if I had miraculously managed to do so, I would only have the consent of the users in my datasets; yet their tweets might have received replies from other users, who might not want to be easily identified.

I believe the eventual answer to such dilemmas defy mere normative ethics as a decision-making process or sets of rules and guidelines: They are moral

108 Research Design philosophy questions, and are completely dependent on the moral frameworks of the person attempting to address them. I am not a philosopher, and this is not a moral philosophy study. My personal approach to the ethical considerations of this research is to privilege the rights of participants over mine. This, of course, requires some level of ethical decision-making and trust on the part of my readers as well.

My ethical stance on participants’ privacy means that I refrained from including the texts of tweets in this thesis. I admit that this decision might hamper the perceived reliability of some of my arguments, but I was willing to take this risk to protect the privacy of others. This is where I share the ethical responsibility with my readers, whom I trust to trust me. The same is true for my decision to anonymise the Twitter handles of some participants when reporting their level of activity (Section 6.5). In order to respect their right to visibility, I decided to only report the user names of the most active accounts in each case; of those from whom I have received consent; or of public figures who have already been made visible in other sources, such as news media.

Research Design 109 5 The Three Case Studies

5.1 Introduction

Chapter 5 introduces the three case studies of the project. I provide a brief account of each case study to familiarise the reader with its broader context.

However, the page limitations of a PhD thesis do not allow me to discuss all the intricacies of each case study; therefore, I provide an overview of each. In cases where more details are required in the interpretation and presentation of the findings, I provide these with thefindings in their respective sections.

5.2 #RoboDebt: An Algorithmic Controversy

Centrelink, a part of the Department of Human Services, is an Australian government agency that is responsible for the distribution of welfare payments such as disability pensions, unemployment benefits, and child care payments.

Around July 2016, Centrelink began using an automated data-matching system to match the agency’s data with the Australian Tax Office’s (ATO’s) data.

The aim was to find discrepancies inthe amounts of money received by individuals, and the income they reported to the ATO. The system, officially referred to as the ‘Online Compliance Intervention’ (OCI), was intended to replace the old manual system of data-matching, and to automate and speed up the process of identifying whether benefit recipients had been overpaid by

Centrelink (Australian Government, 2017). It was also meant to reduce the substantial costs involved in the manual processing of payment records and discovery of welfare fraud.

110 The Three Case Studies In the new automated system, an individual’s records of fortnightly welfare payments in the Centrelink database were matched with their annual tax records in the ATO’s databases. If discrepancies were identified by the newly implemented algorithm, a debt notice was automatically generated and mailed to the individual’s registered address in Centrelink’s records. However, the system started making headlines around December 2016, when a large number of people claimed to have received letters from debt collectors or Centrelink, informing them that they had been overpaid and had to pay the debt thus incurred. Based on some reports, the debts varied from hundreds, to thousands of dollars (Medhora, 2017).

The new OCI system, in effect, shifted the burden of proof to the recipient of the letters, who was then given a limited time to provide the necessary documents, such as pay slips, to resolve the issue. If an individual did not do so in the given time, they would then be contacted by debt collectors. However, at the time the system was implemented, many of the people receiving letters from debt collectors claimed they had not received the initial notices, or that they had not been able to communicate with Centrelink due to long waiting times on the phone (Knaus & Hutchens, 2016; Towell, 2016). At times, the notices were sent to old addresses in Centrelink’s records. In other cases, multiple employers were recorded for a recipient, since Centrelink had used the name of the employer registered with the ATO rather than their ABN

(Australian Business Number). This meant than if an employer’s name was spelt in several ways, that employer was registered as several different employers.

Critics formed a Twitter campaign, referring to the process as ‘robo-debt’, and trending #RoboDebt and #NotMyDebt as the main hashtags to be used in

The Three Case Studies 111 discussions of the matter in the Australian Twittersphere. In a comment piece for Fairfax Media, a blogger provided an emotional and detailed first-hand story of her experience with the process (A. Fox, 2017). Following the article, journalist Paul Malone wrote another piece—also for Fairfax Media—in which he provided extra details about the story, claiming to have received the information from Centrelink (Malone, 2017). This created another wave of criticism, where Twitter users protested Centrelink’s breach of the blogger’s privacy. It was argued that releasing information about the blogger’s personal

Centrelink case was not only in violation of their privacy, but would also have a chilling effect on other individuals who received unjustified debt notices, and prevent them from publicly scrutinising the issue.

Eventually, the situation led to two external investigations through a Senate

Inquiry and a report by the Commonwealth Ombudsman (2017), both of which found several issues and problems in the newly implemented system. The ombudsman report argues that many of the claims by the public and critics were incorrectly reported; at the same time, however, it points out that

Centrelink had not acted transparently and fairly. A set of recommendations were given to, and agreed to by Centrelink to improve the process.

Nevertheless, the OCI system remains in place.

5.2.1 Significance for the Project

The RoboDebt case study has a number of characteristics that made it valuable for this project. First, it is a highly contentious issue that created a large wave of reactions and debate on social media platforms, especially Twitter; this, per se, made it a worthwhile issue for investigation. Regardless of the discourses involved, the content of the tweets, or the nature of the discussions, the very fact that this issue was perceived to be acute enough to react to, and to

112 The Three Case Studies maintain a discussion about for a sustained period of time, made it an intriguing matter for investigation. More importantly, the complex, multifaceted nature of the issue itself acted as a suitable starting point from which to consider it from a discourse-theoretical perspective.

The issue and the events unfolding during the evolution of the debate, gave rise to a number of parallel fields ofdiscursivity. At one level, there was the discourse of social justice, and the argument that an act of injustice was committed against the most economically vulnerable sectors of a society. At another level, one can observe the discourse of trust in algorithms and in organisations that enforced algorithms in the line of argument that emphasised the lack of human oversight in the implementation of the OCI. At yet another level, there are discourses of politics and vertical antagonisms.

More importantly, with regard to the political, the RoboDebt issue can be conceptualised as belonging more to the inherently political nature of antagonism, but not necessarily and directly as a matter of politics, as Mouffe

(2005) theorises it. The Centrelink debate, in other words, transcends the usual party-political antagonisms and identifications, while at the same time is embedded in the broader socio-political sphere. Its directly economic nature, pertaining to the economic well-being of (potentially) all Australians, regardless of their party affiliations or political ideologies, made it a significant case study with regard to the dynamics of discursive struggles.

Guiding the case study from the beginning, was the question of whether such a case reveals any different patterns than a potentially polarising, Political

(capital ‘P’) topic, or whether one can observe intra-cluster amplification of one’s own discourses. However, this case has less to do with the horizontal antagonisms in the Australian Twittersphere, and more to do with the vertical

The Three Case Studies 113 context. The findings from this case study, when compared with the findings of the two other case studies—which are from different socio-political contexts and discourses—allow us to make comparisons and inferences about the dynamics and conditions of discursive struggle, antagonism, and potential agonisms in social media platforms, specifically Twitter.

5.2.2 Data Collection

For this case study, I began the data collection using five keywords8:

- Centrelink/Centerlink: I used both American and Australian spellings

of the name of the agency to account for the various spellings that users

might have used.

- Tudge: Alan Tudge, the minister for Human Services, was a key figure

in the discussions of the issue. Users frequently referred to him in regard

to both the debate and his position in it, and the leaking of personal

information of the blogger to the press.

- Robodebt/#Robodebt: The term ‘robo-debt’ and its hashtag was the

name given by activists to the automated debt notices sent to recipients.

The media also used this term in reporting on the issue.

- NotMyDebt/Not My Debt/#NotMyDebt: Along with robodebt, this

was another term created as part of the protest. A Twitter account and

a website of the same name were also created by activists for people

who had received unjustified debt notices to gather and campaign.

Querying TrISMA for the above terms yielded a total of 407,632 tweets posted between December 2016 and May 2017.

8 I treated all the keywords and the tweets containing them as lowercase to account for all the variations in spelling of the terms.

114 The Three Case Studies 5.3 Section 18C and Freedom of Speech

Australia’s first Racial Discrimination Act (RDA) was passed in 1975. This act, which is administered by the Australian Human Rights Commission

(AHRC), makes it unlawful to discriminate against a group or an individual in certain contexts such as employment, accommodation, access to public spaces, based on their race. A number of amendments have been added to the act since then, such as the addition of Section 9 in 1990 to define indirect discrimination, and the amendment of Part IIA in 1995, to prohibit offensive behaviour against individuals based on their race (Soutphommasane et al., 2015, p. 69).

A particular section—Section 18C—of the 1995 amendments is a matter of long-standing debate in the Australian political sphere. This section of the act makes it unlawful to publicly behave in such a way that is perceived as racially discriminatory against a group or an individual (Australian Government, n.d.).

The text of the act reads:

It is unlawful for a person to do an act, otherwise than in private, if:

The act is reasonably likely, in all the circumstances, to offend, insult, humiliate or intimidate another person or a group of people; and

The act is done because of the race, colour or national or ethnic origin of the other person or of some or all of the people in the group.

The next section in the act—Section 18D—provides details of exemptions to the interpretation of Section 18C:

Section 18C does not render unlawful anything said or done reasonably and in good faith:

in the performance, exhibition or distribution of an artistic work; or

The Three Case Studies 115 in the course of any statement, publication, discussion or debate made or held for any genuine academic, artistic or scientific purpose or any other genuine purpose in the public interest; or

in making or publishing:

a fair and accurate report of any event or matter of public interest; or

a fair comment on any event or matter of public interest if the comment is an expression of a genuine belief held by the person making the comment. (Australian Government, n.d.)

Since its inception, Section 18C has acted as a battleground between the conservative and progressive voices in Australian politics. In general, the RDA does not treat racial vilification as a criminal offence, but as an unlawful act.

The process involved in the handling of 18C complaints starts with a complaint to the AHRC, the body responsible for the initial investigation of the complaints. This complaint is then either dismissed, or a conciliation process begins. If the AHRC cannot resolve the matter, the case can then be taken to court. However, this has only happened in fewer than 5 per cent of cases

(Marlow, 2016). Even in such cases, the damages to be paid by the perpetrators are generally minimal, and far from draconian.

Regardless of the low number of 18C cases that actually find their way to the court system, a number of high-profile cases involving public figures have heavily impacted the debate over whether 18C needs to be altered or repealed.

A 2011 case against a conservative columnist in News Corp, Andrew Bolt, was probably the first major ignitor of the debate (Bodey, 2011). In two articles that Bolt wrote in 2009, he implied that a number of light-skinned Australians self-identified as Aboriginal in order to receive the benefits available to

Aboriginal people. The complaints made by a group of Aboriginal people who were targeted in the articles were upheld by the court, and the newspapers

116 The Three Case Studies publishing the articles were ordered to make corrections to print and online records of the articles. In reaction to the ruling, the then Liberal Party leader

Tony Abbott promised to repeal Section 18C if he became prime minister.

However, after taking this office, he abandoned such plans due to their highly controversial nature (Aston, 2014).

Another 18C case that also had a key role in fuelling the debate was a case against a News Corp cartoonist, Bill Leak. One of his cartoons published in

The Australian newspaper, was deemed to racially vilify the Indigenous community in Australia through amplification of negative stereotypes.The

Australian, among other commentators, defended Bill Leak and the publication of the cartoon, arguing that the case was not an example of racial vilification, but rather the practice of Leak’s free speech (Marr, 2017). Critics, however, believed that the cartoon was racist since it portrayed the negative stereotype of negligent Aboriginal parents.

Finally, a high-profile and widely covered case against a number of students from the Queensland University of Technology (QUT)9 was the third issue that put Section 18C at the centre of debates. The case, filed in 2013 by a staff member at a computer lab in the university, started when a number of non-

Indigenous QUT students were asked to leave an Indigenous-only computer lab. Later, the ousted students allegedly posted racially inflammatory and vilifying comments about the incident on a public Facebook group. After a three-year-long legal battle, the Federal Court of Australia eventually threw out the lawsuit, ruling that the case against the students should not proceed

(Chan, 2016).

9 I acknowledge that while this is the university in which I am doing my PhD, to the best of my knowledge, this did not influence my analysis and interpretations in this study.

The Three Case Studies 117 5.3.1 Significance for the Project

The 18C debate in Australia is highly pertinent to the purposes of this project because of a number of its discursive characteristics. First, it is a case that can be understood as a domestic issue in the Australian political sphere, similar to the #RoboDebt case study. This makes for a useful point of comparison between the two. At the same time, the discursive struggle over Section 18C is deeply embedded within the broader global discourse concerning freedom of speech and its limits. This provided the case study with crucial insights into the complex web of symbolic resources used by each discourse community involved in the debate, and the interdiscursive appeals they made in the struggle over gaining a hegemonic position.

As the brief introduction to the Racial Discrimination Act and the high-profile court cases showed, it is also evident that the debate over Section 18C is simultaneously embedded in the discourses of race and racism in Australia.

Although the debates over 18C have generally been framed as issues of free speech, the fact that such discussions targeted the Racial Discrimination Act rather than other legal settings, shows that the end-goal for the calls for free speech is a desire to have free speech in the context of race-based identity discussions, but not in other contexts. The history of the immigration debate in Australia (which is discussed in Section 5.4) also shows how the topic of race and racism has long been a battleground for the culture wars in Australia

(also discussed in Section 6.3.2).

Additionally, the 18C debate is intrinsically a very polarising one, both because of its discursive connections with the divisive debate over freedom of speech, and because the end-result of the debate can be potentially boiled down to a

Yes/No stance, in which participants eventually choose a ‘side’; that is, arguing

118 The Three Case Studies whether Section 18C should or should not be repealed. Of course, in the argumentation process involved in taking such a stance, a multitude of discourses are invoked, recontextualised, and drawn from. The polarising debate over 18C, therefore, could also serve as a basis for asking an important question for this research: Are the dynamics of discursive struggle any different in a polarised, binary, and inherently (hyper-)antagonistic struggle?

Finally, as a segue into the question of polarisation on social media, filter bubbles, and echo chambers, and as an indicative answer to one of the research questions of this project—inter-cluster dynamics—it is significant to consider such a case, since any investigation of dynamics of interactions and discursive struggles between any two communities has to first start with the most basic question of whether there exists any interaction between the two. Only if there is an information flow (i.e. an absence of filter bubbles) between the antagonistic communities can one inquire about the dynamics of such flows. In this sense, the 18C case study also played a significant role in answering the questions posed in the two other case studies.

5.3.2 Data Collection

For this case study, my initial focus was on the notion of ‘free speech’ and discussions around it in the Australian Twittersphere. I started the query by searching for any tweets in the TrISMA database that contained the phrases

‘free speech’ or ‘freedom of speech’ (with and without spaces). I then undertook a keyword analysis (Section 4.3.3) on periods of time with the highest tweeting activity in the dataset. Different topics were perceived as related to this notion in the dataset. However, the highest number of tweets posted about freedom of speech was related to the discussions of Section 18C of the RDA. These tweets either directly addressed the question of whether or not 18C should be

The Three Case Studies 119 repealed, or were related to the high-profile 18C cases in Australia, such as the case of the QUT students or Bill Leak. Therefore, I chose this debate as the primary focus of the second case study. I queried TrISMA for any tweets containing one or more of the following key terms:

- 18C/S18C/Section 18C: To account for variations in spelling, I included

all possible combinations of ‘18’, ‘C’, spaces, and parentheses [18C, 18

C, 18(C); upper and lower case; with and without # sign; and so on].

- RDA/#RDA: acronym for Racial Discrimination Act.

- Racial Discrimination Act

After cleaning the dataset, removing duplicates and noise, 163,871 tweets that directly discussed Section 18C remained. Another 197,751 discussed freedom of speech, without directly using the list of 18C keywords. I collected these tweets in a separate dataset to be able to differentiate between generic discussions of free speech and those specifically addressing 18C.

5.4 The Immigration Conundrum

The topic of immigration has been a sensitive and controversial matter in the

Australian political sphere for years. Historically, different discourses have been central in the formation and evolution of various positions for and against migration to Australia. In this section, I provide a brief history of immigration to Australia, and the different discourses shaping political and public stances with regard to the issue. While this brief history is necessary to gain a picture of the contextual environment of the third case study, it also helps in framing the other two. As is shown in the following chapters, many of the interdiscursive references and ideological positions regarding all three case studies stem from a common set of discursive resources and nodal points, such

120 The Three Case Studies as the master-signifiers forming discourses around notions such as Australian- ness and nationalism.10

Following the United States’ gaining of independence on 4 July 1776, Britain was faced with the overpopulation of its penal facilities, and was in need of an alternative destination for the transportation of convicts. In 1788, the First

Fleet, consisting of eleven ships carrying convicts from Britain, Ireland, and other British colonies landed in Australia, marking the first European settlement (F. G. Clarke, 2002). The majority of Australia’s first settlers who arrived in the country in the decades following the First Fleet, were transported convicts. However, upon the discovery of gold in Bathurst, New South Wales in 1851, far greater numbers of immigrants moved to Australia from different parts of the world, including Continental Europe, China, New Zealand, and the United States of America (F. G. Clarke, 2002, pp. 55–68).

The six separately governed colonies in Australia federated in 1901, and drafted the Australian Federation Constitution. This meant that since then, the selection of immigrants to Australia was overseen by the Australian

Commonwealth, which started providing assisted passages to groups of immigrants, with a priority given to the British and Irish. One of the first legislations passed by the new parliament was the Immigration Restriction Act, often referred to as the ‘White Australia Policy’ (F. G. Clarke, 2002, p. 97). In effect, this legislation put a stop to immigration from Asian countries for the next five decades.

10 Due to space limitations, I have focused primarily on the voices critical of immigration, and not those promoting multiculturalism and a higher number of immigrants to Australia. However, the general immigration policy of Australia since the 1970s has been supportive of multiculturalism (see, for instance, “Australia’s multicultural policy history” (n.d.), for a brief history).

The Three Case Studies 121 World War I almost stopped migration to Australia in 1914. Domestically, the war also led to classification of residents from the rival countries—such as

Germany, Bulgaria, and Turkey—as enemy aliens, who were either sent to internment camps or severely restricted in their day-to-day life in Australia.

Some nationals—for example, Turkish nationals—were banned from entering the country for some time after the end of the First World War (National

Archives of Australia, n.d.).

Between the first and second world wars, migration to Australia was revived to some extent, and groups of people from around the globe chose the country as their destination. Of course, changes in migration policies of the

Commonwealth, which allowed British ex-servicemen to move to any of the colonies and provided them with assisted passage, still meant that the number of arrivals from Britain was comparatively higher than those from elsewhere

(F. G. Clarke, 2002, pp. 113–117). At the same time, the restrictions that the

USA placed on immigrants from Southern Europe also meant that a greater number of people from Greece and Italy—especially Jewish people fleeing from

Hitler’s horrors—entered Australia.

The Second World War, however, led to a return to the reclassification of existing residents from enemy countries as aliens, again sending them to internment or putting them under close surveillance. At the end of the war, and influenced by Japan’s attack on Australia during the war, there came a radical shift in Australia’s immigration policies. The new approach highlighted the dire need to significantly increase the country’s population so that

Australia could defend the nation against other possible invasions in the future.

The ‘populate or perish’ policy set a minimum threshold of 70,000 immigrants

122 The Three Case Studies per year to meet the required population growth predicted by the newly established Department of Immigration (Price, 1998).

Although the new policies still preferred the new arrivals to be of a primarily

Anglo-Celtic background, the situation in post-war Europe did not allow for such preference. The destruction caused by the war meant that the British government needed as many British as possible to help rebuild the country. At the same time, millions of survivors of the Nazi-occupied European lands were displaced, unable to return to their home countries. The Australian government agreed to welcome around 12,000 of these refugees annually (F. G.

Clarke, 2002, pp. 129–130).

Regardless of the racial and cultural diversity of the new immigrants, the official policy of the Australian government still followed the so-called ‘White

Australia’ principles, and emphasised the need for immigrants’ assimilation into the Australian culture. After the 1950s such policies were gradually relaxed. However, in 1972, the race-based immigration policies were completely replaced by a merit-based system that accepted immigrants according to their skills and occupations (F. G. Clarke, 2002, pp. 168–172). This new immigration system marked the foundation of a multicultural Australia, and is still in place today.

The new system that evolved as the result of the various circumstances discussed above, created a regulated system for accepting different categories of international immigrants to Australia. Very often, the processing of applications takes anytime from six months to a few years. During this process, the applicant continues living in their home country until they receive a visa allowing them to enter Australia as a permanent resident or a temporary worker.

The Three Case Studies 123 Global events after the Second World War, such as the Vietnam war, led at times to the arrival of groups of people from affected regions. These groups often entered Australia by boat and applied for refugee status once they arrived. This group of immigrants, sometimes known as ‘boat people’

(Parliament of Australia, 2001), are a matter of much debate in Australian politics. In 2013, the incumbent Liberal National Coalition started a border protection operation labelled ‘Operation Sovereign Borders’ (Federal Register of Legislation, 2013) to stop this maritime arrival of immigrants. They adopted a zero tolerance policy that results in the mandatory detention of all illegal arrivals in detention camps located in Australia, Manus (in Papua New

Guinea), and Nauru11. In conjunction with this zero tolerance policy, the

Australian government started a large-scale campaign on various international media. This campaign included targeted ads on social media platforms, warning that illegal immigrants arriving in Australia by boat would never be accepted into the country. The Australian government reported an eighty per cent drop in the arrival of ‘boat people’, and the cessation of people smuggling operations

(Ireland, 2014).

Finally, a number of other issues further complicate the already complex discourse of immigration into Australia. These include: the treatment of boat people in Manus and Nauru detention camps; conservative voices within the

Australian political sphere; and the rise of anti-immigration and nationalist discourses around the globe.

The zero tolerance policy adopted by Operation Sovereign Borders has put the

Australian government in a difficult position regarding the treatment of

11 Although this operation started in 2013, it followed precedents set as the result of the ‘Tampa Affair’ in 2001, which marked the start of offshore processing of refugees in Australia (See P. D. Fox, 2010)

124 The Three Case Studies maritime arrivals in the Manus and Nauru detention camps. Numerous reports of the harsh conditions and unfair treatment of the camp residents; the lack of health-care facilities; disease, death, depression, and suicide in the detention centres (Amin & Kwai, 2018); and of a lack of access to the centres by independent journalists, have all put a great deal of domestic and international pressure on the Australian government. Nevertheless, any policy softening in this regard could also put the government at risk of losing the support of a part of their electorate, as it might be perceived as a sign of political capitulation, especially from the point of view of the conservatives and right wing of the political spectrum. Additionally, opening up the Australian borders to the residents of the detention camps and granting them refugee status, also carries the risk of a revival of people smuggling operations.

Finally, the immigration discourse in Australia is also highly influenced by both the more conservative voices within the Australian political spectrum, and by the global changes in public sentiments about immigration. Although post-1970s Australia has a generally welcoming approach to new immigrants, this has not been without pressure and disagreement in the socio-political sphere.

In the 1980s, there was a general sentiment against what was perceived as a high rate of Asian immigration to Australia (Dunn, Forrest, Burnley, &

McDonald, 2004). The One Australia policy, launched by the then opposition leader John Howard, was a reaction to this, and aimed to limit the number of

Asian immigrants to Australia. The same sentiment was reflected by the ultra- conservative senator Pauline Hanson in her maiden speech in 1996; she argued that “…we are being swamped by Asians” (Sydney Morning Herald, 2016). In

2017, Hanson appeared in the Senate during question time, wearing a burqa.

The Three Case Studies 125 She then removed it in a dramatic fashion, before asking senator George

Brandis (the Attorney-General) whether he would support a ban on burqas in

Australia (Remeikis, 2017). More recently, in his maiden speech in 2018,

Senator Fraser Anning claimed that “the one immigrant group here and in other Western nations that has consistently shown itself to be the least able to assimilate and integrate is Muslims” (SBS News, 2018).

Although the targets of such anti-immigration sentiments have frequently changed in the past years—at times being Asians or Italians, and more recently, Africans and/or Muslims (Sparrow, 2018)—the anti-immigration rhetoric has been a constant in the Australian political sphere.

5.4.1 Significance for the Project

The brief historical account of the immigration discourse and policies in

Australia shows the intricacies and complexities involved in the broader discursive environment circling issues of immigration and refugees. The web of discourses formed around the nodal point of ‘Australian’, or who is or is not, or should or should not be an Australian, reveals a multilayered ‘order of discourses’ in Foucauldian terms. Expectedly, these discursive struggles manifest themselves in the dataset of the third case study in this project. What distinguishes this third case from the other two, therefore, is its embeddedness in both the historical and international discourses of immigration, and its multifaceted nature. While the first case study focuses on an Australian socio- economic issue, and the second on an Australian socio-political and hyper- partisan case, the focus of the third is an issue with an array of inter-related discourses. This, in turn, pointed this project to the multitude of available discourses in the field of discursivity, and to how these various assemblages of

126 The Three Case Studies semiotic and symbolic resources are strategically used by different discourse communities in the hegemonic process.

In other words, investigating this case in a comparative fashion with the previous two cases enabled me to draw conclusions that are informed by different combinations of discursive formations. The first case study can be conceptualised as situated more within the vertical than the horizontal context, while the second encompasses both contextual vertices. However, as shown in

Section 5.3.1, the second study was selected because of its highly polarising nature, where the discursive struggle could be reduced to a binary opposition.

The third study, however, focussed on a discourse that cannot be simply reduced to two positions. Rather, it is embedded in a multitude of Australian and international discourses, both on the vertical and horizontal contexts.

Therefore, it acts as a suitable point of comparison for information flows, discursive struggles, and antagonisms between a polarising and a multi- discursive topic.

5.4.2 Data Collection

Given the multifaceted nature of the immigration debate within the Australian

Twittersphere, and the fact that it is intertwined with Australia’s asylum policies, I queried TrISMA for any tweets containing one or more of the following terms:

- (Im)migration/(Im)migrant: the generic terms used for the collection of

tweets discussing the topic of immigration

- Asylum/refugee: to account for tweets discussing the topic of asylum

seekers and refugees

The Three Case Studies 127 - Boat people/boat person: historical terms used to refer to maritime

arrivals to Australia

- Manus/Nauru: the names of the two islands in which Australia’s

detention camps are located

- People smuggler: a phrase widely used in the official discourse about

Operation Sovereign Borders

- CloseTheCamps/close the camps/BringThemHere/bring them here:

phrases and hashtags used by activists, calling for the detention camps

to be dismantled and their residents welcomed to Australia

- Illegal arrivals/illegals: another official term to refer to immigrants who

enter Australia by boat without first applying for visas

After cleaning the dataset, removing duplicates, noise, and spam, a total of

1,073,457 tweets formed the initial corpus for this case study.

128 The Three Case Studies 6 Collective Patterns of Activity

You just stood there screaming Fearing no one was listening to you They say the empty can rattles the most The sound of your own voice must soothe you Hearing only what you want to hear And knowing only what you’ve heard You You’re smothered in tragedy And you’re out to save the world (Hetfield, Ulrich, & Newsted, 1991) 6.1 Introduction

This chapter investigates the aggregate and collective patterns of communication enabled by the affordances of Twitter. I start with an overview of the temporal metrics of activity for each case study, in which I examine the ebbs and flows in tweeting levels in light of the broader socio-political context.

This section also explores how different communities of users tweet in reaction to different events and news.

I then move my attention to hashtags, as a key affordance of Twitter. I first analyse the overall patterns in the collective use of hashtags by the Twitter users discussing the issue in each case study. I then focus on the differences in the use of hashtags of the antagonistic discourse communities involved in the debate.

This chapter continues with a focus on the various URLs shared in each case study, to gain a better understanding of how the discussions on the platform are embedded in the broader vertical context. Additionally, I explore the divergent patterns of link-sharing by different discourse communities, in order to investigate how URLs are used as discursive strategies and articulatory

Collective Patterns of Activity 129 practices to serve the amplification of discourses, and whether there are potentials for an agonistic space in this regard.

Moving on, I turn my attention to the metrics of communication that influence a user’s visibility on the platform. This section provides insights into the perceived discursive functions achieved by various visibility-inducing metrics, such as the level of activity, retweets, and @mentions. Furthermore, it shows how the collective employment of these affordances leads to different articulatory practices in different situations.

Finally, this chapter concludes with a light-weight discourse-theoretical interpretation of the findings of this stage of the analysis through their synthesis and summary. The more in-depth interpretation of the findings is only possible once the other stages of the analysis have been discussed.

Therefore, I return to a detailed interpretation of this chapter in Chapters 8 and 9.

6.2 Tweets over Time

6.2.1 #RoboDebt

Time-series analysis of tweets posted during the evolution of the #RoboDebt discussion revealed several periods of heightened tweeting activity. The largest peaks of tweeting on the issue appeared at the initial stages of the debate, which started in early January 2017. Initial news of the errors and flaws in the newly implemented automated debt recovery system received widespread reactions from Twitter users: Over 60,000 tweets posted in the first few days after a Sydney Morning Herald article on 4 January 2017, pointed to the large number of erroneous debt notices (Martin, 2017).

130 Collective Patterns of Activity Following the initial news, ABC Radio National interviewed Alan Tudge, the then Minister of Human Services, who argued that the system was efficient, and that the debt recovery process would continue (Yoo, 2017). Twitter users, however, were not convinced by these arguments and continued to extensively criticise the interview.

The other peaks of tweeting activity with regard to the Centrelink controversy all seemed to follow the events and news unfolding in the period under investigation. The tracing of each peak in the time-series analysis back to its contextual environment, pointed to the fact that events unfolding in the broader socio-political context act as triggers of tweeting activity.

Figure 6-1: #RoboDebt tweets over time Following the initial peaks in early January 2017, the next major tweeting activity occurred around 20 January, when a whistleblower from Centrelink revealed that staff were aware of the discrepancies in the data-matching algorithm, but were instructed not to address them (Knaus, 2017a; McGrath,

2017). During the same time, a blogger wrote a piece detailing her negative experience in the process of receiving a false debt notice. She had difficulty in

Collective Patterns of Activity 131 contacting Centrelink, and received very bad service from the staff when she eventually did (A. Fox, 2017). On 24 January, an article in The Sydney

Morning Herald revealed the personal information of the blogger, claiming that she had misrepresented the story and that she was, in fact, in debt to

Centrelink (Malone, 2017).

It was later revealed that the journalist had received the blogger’s personal information from the Department of Health Services (Knaus & Farrell, 2017).

This created another wave of reactions to the news (the early February peak).

In late February and early March, more news was published about this, with activists referring to it as a doxxing12 practice, and calling for legal action against Alan Tudge (Bogle, 2017). Their argument was that revealing the personal information of the blogger was a breach of her privacy, and that it was used as a threat to others who were critical of Centrelink. The government took a strongly defensive position in this regard, justifying the action as necessary in order for the public to hear both sides of the story (Knaus &

Farrell, 2017).

Later, further news detailed how one of the recipients of a debt notice had committed suicide (McKenzie-Murray, 2017), and a Senate Inquiry was initiated (Knaus, 2017b). In early April, there were news and opinion pieces on how Alan Tudge might have committed a crime by disclosing the blogger’s personal information to the press (Belot, 2017). Finally, the Ombudsman published their report on 10 April, reporting that Centrelink’s algorithmic

12 Doxxing (also ‘doxing’; abbreviation for ‘documents’) is the practice of releasing someone’s identifiable and/or personal information online, often to incite violence against, and harassment of them (see, for instance: Quodling, 2015).

132 Collective Patterns of Activity data-matching had serious flawsand required human oversight

(Commonwealth Ombudsman, 2017).

6.2.2 Section 18C

Similar to the #RoboDebt case, discussions of Section 18C in the Australian

Twittersphere also followed the breaking of news and events over time, although with some differences. Overall, the size of the dataset was smaller than the #RoboDebt dataset. This could indicate a lower perceived sense of acuteness of the topic in the Australian socio-political sphere. This, of course, is not particularly surprising, since the scope of the discussion does not inherently apply to all members of society.

The nature of the #RoboDebt controversy—in being a more economic, and less political debate; being directly related to the well-being of a large number of Australians; and having a number of highly active curators—led to its being more widely discussed than the topic of free speech. Section 18C of the RDA is more in the sphere of politics, only interesting for politically-engaged Twitter users. Another point of difference between the two cases is that the #RoboDebt controversy has less to do with identities, opinions, party affiliations, and ideologies. Rather, it is a case of welfare, economy, and social justice. On the other hand, the debate over freedom of speech and Section 18C is directly tied to existing political orientations, identifications, and ideologies, where an individual is assumed to have a position, or expected to ‘take a side’. The main discursive struggle over a hegemonic discourse, in the case of Section 18C, can be boiled down to taking a position on whether this particular section should be repealed or not.

Collective Patterns of Activity 133 The examination of tweets posted over time pointed to a number of distinct peaks of activity in the Twitter discussions of Section 18C. The first period of heightened tweeting activity in this regard coincided with two events in the

Australian political sphere. A cartoon published on Aboriginal and Torres

Strait Islander Children’s Day in The Australian news outlet was deemed to be portraying racial stereotypes (Melbourne, 2016). The cartoon shows an

Aboriginal police officer holding a young Aboriginal child by the collar and talking to his dad, who is holding a beer can and cannot seem to remember the name of his own son. The then Indigenous Affairs Minister, Nigel Scullion, condemned the cartoon and called it racist in nature (U. Patel, 2016a). Later, an Indigenous Australian student filed an 18C complaint against Bill Leak with the Australian Human Rights Commission, arguing that the cartoon made her feel “…pretty degraded and humiliated”, and that she was discriminated against and intimidated as a result of the cartoon (McCormack, 2016). During the same time, the first news about a call to make changes to Section 18C of the RDA was published, with some politicians going on record to say that they would support such changes (U. Patel, 2016b).

In early November 2016, one of the high-profile 18C cases in Australia—Cindy

Prior vs Queensland University of Technology and others—was thrown out, with the judge ruling that the proceedings against QUT students must be dismissed, and the lawsuit should not proceed. At the same time, news broke that the Australian government was to initiate an inquiry into Section 18C in the parliament. A qualitative exploratory reading of the tweets posted during this time showed that the observed peak was due to Twitter users’ reactions to this news.

134 Collective Patterns of Activity In late February 2017, the Australian parliament reported the outcomes of its inquiry into Section 18C (Gelber, 2017). The report focused on the procedures and processes involved in handling 18C cases, and did not provide any conclusions on whether or not there should be any amendments to the section.

Following this report, backbench senators again pushed for changes to, or a total repeal of the section (Holman, 2017).

Finally, a number of different events in mid- and late March 2017 triggered the highest peaks of tweeting activity in the dataset. The death of the controversial cartoonist Bill Leak (10 March 2017) once again brought the discussion of free speech and Section 18C to the surface, with his supporters calling him a hero of free speech and a victim of the 18C. They created a

Change.org petition in his memory, and thousands signed the petition to force the government to make amendments to Section 18C (Burton-Bradley, 2017).

On 21 March—the United Nation’s International Day for the Elimination of

Racial Discrimination—the Australian government presented changes to

Section 18C for debate in the parliament. The ironic choice of this day—known as ‘Harmony Day’ in Australia—to debate changes to the country’s racial discrimination laws, caused major reactions in the Australian Twittersphere.

Eventually, on 30 March, the Australian Senate blocked the government’s proposed changes, and Section 18C remains intact.

Collective Patterns of Activity 135

Figure 6-2: Section 18C tweets over time As can be seen from the discussion above, the peaks of activity (Figure 6-2) can be traced to Twitter users’ reactions to breaking news in the vertical context of the discussion, in a similar fashion to the #RoboDebt case. However, the inherently divisive and polarising nature of the 18C debate led to a number of differences in how different communitiesof users reacted to similar news and events.

Chapter 4 discussed how this research project employed a circular methodology, in which the findings of each phase informed the other stages of the study, and indicated points of interest for further investigation. The intrinsically polarising nature of the 18C debate, along with the findings from the social network analysis (that are discussed in the next chapter) led me to the identification of different discourses, communities, and tweeting patterns in the debate. Unlike the #RoboDebt case study, where users from different clusters in the Australian Twittersphere shared the same discourses, symbolic resources, and arguments, the 18C case was primarily a discursive struggle between two antagonistic communities in the Australian Twittersphere:

Progressive Politics and Hard Right Politics. To investigate the dynamics of

136 Collective Patterns of Activity this discursive struggle, I separately examined the tweeting activity of users in the Progressive and Hard Right clusters in the map of the Australian

Twittersphere (Section 3.7)13.

Overall, the level of activity of both communities followed the timeline of events discussed above. However, there were a few exceptions to this broad pattern. The first major difference in eth level of tweeting by Progressive and

Hard-Right users appeared on 4 November 2016. This was the only instance in the timeline of the discussion where the number of tweets posted by Hard Right users (577 tweets) exceeded those of the Progressive community (544 tweets).

While the difference between these numbers might not seem very great, two points make it particularly significant. First, the fact that this day marked the only point in the timeline where this inversion appeared by itself indicated that it was worthwhile to further investigate its cause. Second, and more importantly, this disparity shows its significance when one considers the large difference between the number of ccountsa in the two communities. The

Progressive Politics cluster in the Australian Twittersphere has 4,024 accounts, while the Hard-Right cluster is almost three times smaller, with 1,443 accounts.

Figure 6-4, which presents tweeting levels of the communities normalised by their population size, shows this difference more clearly.

13 My analysis of the discourse communities in the Australian Twittersphere in this chapter relied on the latest published iteration of the network (Bruns, Moon, Münch, & Sadkowsky, 2017), which focuses on the globally best-connected 250,000 accounts in the Australian Twittersphere. Therefore, my analysis excluded the less-connected accounts. This introduced some limitations; I discuss these in Section 3.8.

Collective Patterns of Activity 137

Figure 6-3: Section 18C tweets over time by antagonistic clusters

Figure 6-4: Section 18C tweets over time by antagonistic clusters, normalised by the number of accounts in each cluster in the Australian Twittersphere The qualitative examination of tweets posted on this day by the Hard-Right users showed that this peak of activity was caused by their reaction to the

Federal Circuit Court’s decision to throw out the 18C case against the three

QUT students. This news led to major reactions from ultra-conservative users:

They widely shared the news among their community; criticised it; and called for the repeal of 18C. However, this news did not make any meaningful

138 Collective Patterns of Activity differences in the number of tweetsposted by the Progressive Politics community in the Australian Twittersphere. Another visible difference in the level of tweeting activity between the Progressive and Hard-Right communities became apparent on 7 November 2016. It coincided with the announcement of a parliamentary inquiry into Section 18C, and the AHRC president’s support for the inquiry. He argued that a change in the wording of the 18C could potentially strengthen it. Reactions to this news were stronger in the

Progressive Politics cluster than the Hard-Right.

Also, on 12 December 2016, The Australian published an article that quoted the submission made by the Institute of Public Affairs—an infamous

Australian conservative think tank—to the parliamentary committee investigating 18C laws (Shanahan, 2016). The article, arguing that keeping

Section 18C equalled the end of free speech in Australia, created a wave of negative reactions from the Progressive community. The Hard-Right community also shared this article widely through retweets; however, no major changes were seen in the level of original tweeting activity by the Hard-Right community on this day.

6.2.3 Immigration

A number of key characteristics made the third case study different than the first two. First, both the #RoboDebt and Section 18C case studies are constrained to the Australian socio-political environment. In the case of the discussions of immigration, however, the discourse is tied to both domestic debates and international discourses. The rise of anti-immigration sentiments globally, the so-called ‘refugee crisis’ in Europe, the election and immigration policies of Donald Trump in the United States, and the increasing power of right-wing and populist politicians in different countries, intertwine the

Collective Patterns of Activity 139 Australian immigration discourse with these global discourses. Second, even within the domestic discursive environment of Australia, the topic of immigration is discussed and viewed from different perspectives.

Historically, being an immigrant-populated country, Australia has always been engaged in some form of debate over its immigration policies. In the past few years, this has included debates over the number of immigrants, asylum policies, the detention of illegal immigrants in offshore camps, and changes in the granting of permanent residence visas. This complex discursive environment makes the immigration discourse in Australia a potentially multipolar debate, in which different ctors,a communities, and institutions rely on their own discourses and symbolic resources to discuss the issue. This complexity is also evident in the temporal dynamics of the debate in the third case study of this research project.

Overall, the volume of tweets in the dataset shows the significance of the topic of immigration for Australian Twittersphere users. In total, 1,028,955 distinct tweets using one or more of the keywords used in the data collection were posted between February and September 2018. On average, between 2,000 and

5,000 tweets per day were posted on the topic of immigration. Similar to the other case studies, the level of tweeting activity was directly related to the events of the day, and the unfolding of news in the broader context. However, given that the discussions of immigration are multipolar, domestic, and international at the same time, the contextual triggers for increased tweeting activities were varied.

The first major peaks of activity in thetimeframe occurred in mid and late

March 2018. In mid-March, Peter Dutton, the then minister for Immigration and Border Protection, received widespread criticism over his plans to resettle

140 Collective Patterns of Activity white South African farmers in Australia as refugees (Taylor, 2018). During the same period, he was under scrutiny over reports that showed he had used his ministerial powers to intervene in granting a visa to an au pair who was being held at Brisbane Airport and was to be sent home due to the fact that she was in breach of her visa requirements (“Peter Dutton defends his decision to grant visa to detained au pair,” 2018). It was later revealed that Dutton had intervened in two separate au pair cases. Additionally, on 25 March 2018, coordinated rallies were organised in different Australian cities to protest against the offshore detention camps (Power, 2018). These events all led to an increased level of tweeting in March 2018, with tweets sharing news about the events, discussing this news, and showing solidarity with the residents of offshore detention camps in Manus and Nauru.

In late May 2018, news about the death of a Rohingya refugee in Nauru, and doctors’ pleas to the Australian government to allow a terminally ill detainee to be transferred to Australia, again led to peaks of tweeting in the Australian

Twittersphere (Butler, 2018). At the same time, more news about the au pairs case was published, accusing the head of the Home Affairs department of

“covering up” Dutton’s abuse of his ministerial powers in the au pair visa decisions (Davidson, 2018).

Collective Patterns of Activity 141

Figure 6-5: Immigration tweets over time The highest peak of activity with regard to tweets about immigration occurred on 20 June 2018, World Refugee Day. Given the controversial position of the

Australian government regarding illegal maritime arrivals, and their indefinite detention in offshore camps, Twitter users and activists used this day to voice their dissent, protest about the detention camps, and show solidarity with the residents of the detention centres in Manus and Nauru. During the same week, news of Donald Trump’s decision to end the separation of immigrant families was also published, and this also contributed to the high level of tweeting in that period (“Trump signs executive order to end separation of immigrant families,” 2018).

This particular day was also significant with regard to the multipolarity of the immigration debate in the Australian Twittersphere. Similar to the Section

18C case study—where news that resonated more with a particular discourse led to more reactions by users in that cluster, but caused no meaningful changes in the activity levels of other clusters— the same pattern of activity was evident on World Refugee Day. While this day marked the highest peak of tweeting activity with regard to immigration discussions, separating the active

142 Collective Patterns of Activity accounts in terms of their discourse communities, showed that this rise in activity was largely due to the concerted tweeting of the Progressive discourse community. On the other hand, the level of activity in the Hard-Right cluster dropped below its average on this particular day.

On observing the similarities of the tweeting activities in all three cases, and the way they almost perfectly match the events and news in the broader discursive environment, the embeddedness of the platform within its context became more pronounced. While patterns of activity follow the events in the socio-political context, different levels of reaction to various events by different discourse communities also point to the discursive nature of reactions. In other words, there is always a level of calculated discursive choice-making in users’ reactions to events in the vertical context, in that a decision is made regarding what (not) to react to. At the same time, these choices (agency) are always influenced by the socio-political structures within which an actor is operating.

Furthermore, the discursive practices of Twitter users are simultaneously constrained by the material affordances of the platform. In the following sections, I examine these material affordances and the knotted relationship

(Carpentier, 2017) between structure, agency, discourse, and material.

6.3 Hashtags

As one of the main material affordances of Twitter, hashtags can perform a range of communication and discursive functions. In broadest terms, a hashtag enables the creation of what Zappavigna (2015) calls ‘searchable talk’. Using a hashtag in a tweet enables any other user to click on the hashtag and see all the other tweets that contain it. Also, the collective use of a hashtag can serve as creating a sense of ‘ambient affiliation’ in users (Zappavigna, 2011). Users respond to different issues with hashtags, creating ‘momentary connectedness’

Collective Patterns of Activity 143 (Rathnayake & Suthers, 2018) among users within a topical network. The momentary connectedness created by the hashtag, in turn, creates ‘ad hoc’ and

‘calculated’ issue publics (Bruns & Burgess, 2015, 2011). Additionally, Twitter hashtags can serve as discursive strategies in themselves, or markers of the discourse of the tweet in which they are employed (Giglietto & Lee, 2017). At the same time, hashtags can be the site of struggle between the discourses of the platform and its users, where they can show the platform moderators—as well as discursive antagonists—“where the problematic content can be found”

(Gerrard, 2018). Therefore, the act of (not) using a hashtag can, per se, be a discursive and articulatory practice, performed using the material affordances of the platform. In this section, I focus on how hashtags are collectively used in the three case studies of the research project.

6.3.1 #RoboDebt

Aggregate analysis of hashtags in the #RoboDebt dataset revealed approximately 9,000 distinct hashtags, used in a total of 394,354 tweets. Of course, the majority of these hashtags were only used in a small number of tweets; this is itself an indication of the relative success of activists and influencers in creating a uniform hashtag public (Bruns & Burgess, 2015) where the issue is discussed. However, particularly interesting for this project, are the hashtags that serve as discursive strategies, and that are widely used by a large number of users.

Table 6-1: Top hashtags in #RoboDebt

Hashtag Count #notmydebt 110,583 #centrelink 65,361 #auspol 61,855 #robodebt 10,777 #centrelinkfail 9,289 #leybuy 4,756

144 Collective Patterns of Activity #lnp 3,035 #turnbull 2,984 #qanda 2,685 #welfare 2,633 #censusfail 2,103 #qt 1,732 #not 1,625 #dignitynotdebt 1,479 #tudge 1,423 #au 1,400 #notmyd 1,229 #rorts 1,195 #abc730 1,136 One such hashtag in this case is #CensusFail, used in 2,103 tweets in the dataset. This hashtag, particularly when seen in conjunction with similar ones—such as #CentrelinkFail (9,289 tweets), #LNPFail (914 tweets),

#TudgeFail (479 tweets), and other ‘fail’ hashtags—points to a collective attempt at bringing other discourses that users perceive as relevant to the foreground of the conversations. The ‘-fail’ suffix in these hashtags is an interdiscursive reference to an earlier issue in the Australian socio-political public sphere. In 2016, the Australian government attempted to do the national census using an online portal for the first time. However, a DDOS (distributed denial of service) attack on the website on 9 August caused the census system to collapse and be out of service for 40 hours (Killialea, 2016). This, along with privacy concerns raised by experts about using an online system to collect personal information at a national scale, was referred to as ‘Census Fail’ by

Twitter users, and #CensusFail became a trending topic in the Australian

Twittersphere at the time.

The use of #CensusFail half a year after the issue, or the suffixing of other individuals and political parties with the term ‘fail’ in the discussions of

#RoboDebt, should be seen as calculated interdiscursive references, connecting the Centrelink controversy to other failed attempts by the Australian

Collective Patterns of Activity 145 government to use a technological solution to a problem. The process of recontextualising the census failure in light of the new controversy, therefore, borrows from a shared symbolic resource, where Twitter users tap into their collective memory, using similar cases in painting a new picture. Such cases of interdiscursivity are frequently present in the most used hashtags in the dataset, where users juxtapose the primary hashtag (#RoboDebt) with secondary hashtags to strategically frame the debate by connecting it to other discourses such as #privacy (911 tweets), or positioning the issue within a broader discourse such as politics. #AusPol (Australian Politics), as the most well-known political hashtag in the Australian Twittersphere, was used in

61,855 tweets discussing the Centrelink controversy. This clearly shows the perceived discursive formation among the users tweeting about the issue, in that they marked the topic as belonging to the discourse of politics, and not simply a technological oversight that should be addressed.

6.3.2 Section 18C

The data collection for this case study included the keywords ‘18C’ and ‘RDA’

(with and without the # sign), so it was expected that these hashtags should have the highest numbers in the list of the most used hashtags. Secondary hashtags, however, are of particular interest in this section, since they show how Twitter users strategically use hashtags as discursive strategies to mark the topicality of their tweet, make interdiscursive references, and so on.

In total, 5,124 distinct hashtags were used in 118,901 tweets in the dataset.

The most widely used hashtags in the discussions of Section 18C (apart from those used in the data collection) are political markers such as #AusPol

(Australian Politics), #QT (Senate Question Time), and #LNP (Liberal

National Party); these mark the perceived position of the debate in the

146 Collective Patterns of Activity discourse of politics. Other widely used secondary hashtags are references to

Australian political television programmes such as #QandA, #Insiders, and

#TheDrum, and to a number of other topic and discourse markers such as

#HarmonyDay, #Racism, #QUT, and #SSM (Same Sex Marriage).

Table 6-2: Top hashtags in 18C

Hashtag Count #18c 34,226 #auspol 25,337 #qanda 4,256 #freedomofspeech 2,749 #insiders 2,156 #rda 1,927 #harmonyday 1,737 #s18c 1,667 #qt 1,412 #racism 1,234 #thedrum 1,151 #freespeech 1,122 #qut 788 #abc730 703 #ssm 597 #freedomofs 578 #lnp 511 #turnbull 500 #billleak 434 #auslaw 380 However, as the other stages of analysis showed, the discursive space in which

Section 18C is discussed is an inherently polarised one, where outcomes of the discussions are expected to mean taking a stance between keeping 18C intact or amending/repealing it. Therefore, it is worth focusing on the dynamics of the use of hashtags within antagonistic discourses and communities to have a clearer understanding of how hashtags are used, and the role they play in the discursive struggle over freedom of speech.

Comparing the use of hashtags by the Progressive and Hard-Right communities—the main clusters active in the discussion—clearly shows how

Collective Patterns of Activity 147 hashtags are employed as discursive strategies to amplify one’s discourses, frame the debate, or connect the discussion to other discourses. The Progressive community, for instance, frequently employs #Racism in their tweets, framing the debate from the perspective of racism and anti-racism. The debate is also interdiscursively recontextualised as being related to the Australian government’s failure to embrace progressive values, and to opt for increasingly conservative stances instead. This was evident from the use of secondary hashtags such as #SSM or #IPA. #SSM—Same-Sex Marriage— which was also a matter of attention, debate, and polarisation in the Australian political sphere. The Australian government resisted legalising same-sex marriage for a long time, until an unprecedented postal survey eventually led to its legalisation in 2017 (Koziol, 2017).

While the debate over marriage equality was not directly related to the discussions of Section 18C, users in the Progressive discourse community in the

Australian Twittersphere strategically made interdiscursive links between the two, emphasising the Australian government’s conservativism, and calling for change. This line of criticism stems from the continuing tension between the conservative and progressive voices in the Australian socio-political sphere, sometimes referred to as ‘culture wars’. The culture wars, starting with the tenure of John Howard (Liberal-National Coalition) as Australia’s prime minister, generally manifests itself as a struggle between traditional Christian

Western values and progressive values (see, Gillard, 2003; Johnson, 2007;

Rudd, 2006).

The other secondary hashtag, #IPA, serves the same purpose, through marking the role of the Institute of Public Affairs (a conservative think tank)

148 Collective Patterns of Activity in the debate, and arguing that the push to change Section 18C is directed by lobbying activities of this institute.

Table 6-3: Top hashtags by the Progressive cluster in 18C

Hashtag Count #18c 13,301 #auspol 10,954 #qanda 1,604 #insiders 1,263 #rda 1,025 #freedomofspeech 956 #qt 868 #s18c 689 #harmonyday 610 #thedrum 590 #racism 489 #abc730 432 #turnbull 317 #ssm 311 #lnp 303 #freespeech 248 #ipa 222 #18d 196 #istandwith18c 196 #qldpol 186 On the other hand, the Hard-Right discourse community employ other competing hashtags in their tweets. Hashtags such as #Repeal18C,

#SackTriggs, and #AxeHRC, for instance, serve as clear markers of one’s position in the debate. At the time, Gillian Triggs was the president of the

Australian Human Rights Commission (HRC), which was responsible for handling complaints under Section 18C (Section 5.3). Framing the discussion through the frequent use of secondary hashtags related to high-profile 18C cases in Australia, such as the #QUT or #BillLeak court cases (Section 5.3), this community called for amendments to the country’s Racial Discrimination

Act.

Collective Patterns of Activity 149 In other cases, Hard-Right users amplify their differences with the antagonistic community by marking their own enemy. Unlike the Progressive community, which puts the blame and bias on the IPA, this hashtag is practically absent from the discourse of the Hard-Right. Instead, this community frequently uses

#HRC to shift the blame onto the Human Rights Council of Australia.

Similarly, in referencing Australian political television programs such as

Insiders, or discussing news published by the Australian Broadcasting

Corporation (ABC News), the Hard-Right often emphasises this antagonism by distancing themselves from the discourse of these programs, or by using hashtags such as #Outsiders (another Australian TV program that positions itself against Insiders) or #TheirABC.

Table 6-4: Top hashtags by the Hard-Right cluster in 18C

Hashtag Count #auspol 2,239 #18c 2,215 #freespeech 301 #repeal18c 210 #qut 196 #pmlive 189 #billleak 172 #freedomofspeech 141 #qanda 133 #axehrc 89 #theboltreport 85 #theirabc 80 #ahrc 70 #sacktriggs 63 #viewpoint 61 #hrc 60 #Section18c 58 #abetterway 54 #s18c 53 6.3.3 Immigration

The most shared hashtags in the immigration dataset show the multipolar nature of the debate. Other than the hashtags used for the collection of data

150 Collective Patterns of Activity in this case study, #AusPol is the most widely used hashtag in the dataset.

Additionally, a large number of hashtags point to the prevalence of the discourse of refugees and asylum seekers in the discussions of immigration in

Australia. Hashtags such as #KidsOffNauru, #WithRefugees, #EvacuateNow, and #Justice4Refugees, which are among the top 20 secondary hashtags shared in the dataset, position their users in the community supporting refugees in detention camps. On the other hand, many secondary hashtags interdiscursively connect the debate to non-Australian issues and topics.

Among the list of most-shared secondary hashtags, there is extensive use of hashtags referring to other countries and contexts such as #Syria, #Palestine,

#UK, #Brexit, #Russia, and #Germany. The multipolar and varied use of secondary hashtags, in this case, necessitated further investigation to examine the different discourses and communities using certain hashtags. In other words, other than the aggregate collective metrics examining what was shared, it was significant here to also delve intowho shared it.

Table 6-5: Top hashtags in Immigration

Hashtag Count #auspol 50,987 #nauru 49,801 #manus 38,753 #refugees 21,513 #bringthemhere 10,476 #refugee 10,179 #qanda 9,110 #kidsoffnauru 8,420 #dutton 8,105 #immigration 7,203 #australia 6,420 #worldrefugeeday 5,879 #asylum 4,835 #asylumseekers 4,543 #withrefugees 4,445 #insiders 4,276 #evacuatenow 3,792 #humanrights 2,872

Collective Patterns of Activity 151 #trump 2,831 #rohingya 2,633 In a similar way to the Section 18C case study, this stage of analysis was also informed by the findings of the network analysis (which I cover in the next chapter). An examination of the most active clusters in the Australian

Twittersphere that tweeted about immigration showed that the two primarily involved discourses—as in the 18C case study—were the Progressive and Hard-

Right clusters. Although these antagonistic communities were not the only ones involved in the immigration debate, they were the most active ones.

Therefore, in this section, I focus on the use of hashtags within these two communities, and return to the other active clusters in the next two chapters.

Focusing on the use of primary and secondary hashtags within the Progressive discourse community, it is evident that the signifier marking the discourse of this group of users (i.e. the nodal point of the discourse) is the topic of offshore detention camps. In other words, any other discussion of immigration in the

Australian socio-political public sphere is interdiscursively connected to the

Australian government’s mistreatment of refugees and asylum seekers. Among the most shared hashtags by the Progressive community, the majority are related to the discussion of refugees and asylum seekers, showing solidarity with them, and calling the Australian government to action by asking for the camps to be closed, and for the refugees to be welcomed into Australia.

The only references to non-Australian contexts in this community’s most shared hashtags are interdiscursive references to #Trump, #Rohingya, #Syria, and #Palestine. This strategic use of interdiscursive hashtags supplements the finding that the nodal point of the discourse of the Progressive community with regard to immigration, is the issue of refugees and asylum seekers. As a case in point, a qualitative reading of tweets with the secondary hashtag #Trump

152 Collective Patterns of Activity shows that in cases where this hashtag is used along with a primary one in the discussion of immigration, it is done so as to juxtapose Trump’s anti- immigration policies with the Australian government’s treatment of refugees.

Trump’s policies—such as the so-called ‘travel ban’, building a wall at the US–

Mexico border, or the separation of refugee children from their families—are often compared to Australia’s treatment of refugees in the offshore detention camps. Other non-Australian hashtags—such as interdiscursive references to

Syria, Rohingya, or Palestine—are used to contextualise the discussion in light of the plight of war-stricken, displaced, and nation-less refugees. These references led to the argument that the majority of asylum seekers on Manus and Nauru are people from similar places and should, therefore, be allowed into

Australia.

Table 6-6: Top hashtags by the Progressive cluster in Immigration

Hashtag Count #nauru 21,360 #auspol 19,908 #manus 16,616 #refugees 8,118 #bringthemhere 3,987 #dutton 3,539 #refugee 3,199 #qanda 3,169 #asylum 2,566 #kidsoffnauru 2,300 #australia 2,214 #asylumseekers 2,077 #insiders 1,700 #immigration 1,495 #evacuatenow 1,318 #worldrefugeeday 1,095 #humanrights 988 #libspill 988 #morrison 927 #closethecamps 865 Conversely, the use of hashtags by the Hard-Right discourse community shows stark contrasts with the Progressive cluster. First, discussions of asylum

Collective Patterns of Activity 153 policies in Australia are much further down the list of top hashtags in this community. This is especially significant since keywords such as ‘Manus’,

‘Nauru’, and ‘Refugees’ are among the list of terms I used to collect the data.

Therefore, such terms were expected to be high on the list; however, their absence from the list of most shared Hard-Right hashtags shows that this community of users actively avoids using these terms.

The second difference in the use of secondary hashtags by the Progressive and

Hard-Right community, is the nodal points of the discourses. Unlike the primarily Australian focus of the Progressive cluster, the Hard-Right community draws from the international discourses around immigration. Other than #AusPol, standing at the top of the list of most shared hashtags by this community, the majority of secondary hashtags are related to non-Australian contexts. Hashtags such as #Sweden, #SVPol (Sweden politics), #Poland,

#UK, #EUPol (European Union politics), and the like are frequently used in the discourse of the Hard-Right. Apart from secondary hashtags referring to non-Australian geographical locations, #Islam and #Muslim are also frequently used by the Hard-Right. Australian discussions of immigration and refugee policies, however, are further down the list, with #Manus being 19th, and Nauru 30th.

Table 6-7: Top hashtags by the Hard-Right cluster in Immigration

Hashtag Count #auspol 3,008 #sweden 648 #svpol 635 #poland 629 #uk 612 #eupol 574 #maga 568 #cndpoli 513 #merkel 508 #greece 496

154 Collective Patterns of Activity #germany 491 #ukraine 480 #norge 454 #danmark 449 #serbia 443 #russia 422 #afd 418 #italy 412 #manus 393 #islam 341 Overall, the analysis of the different patterns in the use of hashtags in each case study and by different discourse communities, showed how Twitter users collectively make strategic discursive choices regarding what hashtags to use, and what hashtags to actively avoid. Hashtags are frequently used to mark one’s discourse and political orientation. Furthermore, through supplementing a primary, topical hashtag with a secondary one, users interdiscursively recontextualise different discourses and discursive formations to amplify their voice, give visibility to their discourse, and to articulate different discourses in relation to each other, further sedimenting the nodal points of their discourses.

The divergent patterns in the use of hashtags between the antagonistic communities show how each community draws from a broader discursive formation, with its own logics and symbolic resources, to frame each debate from a particular perspective. In this sense, the symbolic meaning of a hashtag, as a material affordance of Twitter, is consistently negotiated by different users, not only to perform different discursivestrategies, but also to articulate the users’ identities.

6.4 Information Sources

In Sections 4.3.1 and 6.1, I discussed how the use of URLs in a tweet can point to the articulatory practices of different users and communities, and the information sources they draw from, or rely on, when discussing an issue.

Collective Patterns of Activity 155 Information sources used by different collectives, in effect, can mark the discourses shared by those collectives, and what they view as ‘real’ or ‘facts’.

The same principle seems to partly be the driving force behind the distribution and dissemination of so-called ‘fake news’ on social media platforms. Different studies show that in some cases, the distribution of disinformation is not simply the result of a lack of awareness of reliable information sources or the algorithmic curation of personalised news for users (Tucker et al., 2018).

Rather, it is a discursive strategy by individuals to influence the public debate.

As a survey by Pew Research Center shows, for instance, 14% of the respondents reported that although they were aware that what they were sharing was not factual, they shared it anyway, in order to achieve a certain purpose (Mitchell, Barthel, & Holcomb, 2016). Among the reasons reported by this group were the intentional spread of misinformation, “calling out” fake stories and, simply, amusement (ibid.).

In this light, an investigation of the URLs shared in the discussions of different topics and by different discourse communities guided this study to a better understanding of the dynamics of antagonism and agonism, achieved through the articulation of hyperlinks to information sources. In this section, therefore,

I examine how users collectively and strategically rely on, and disseminate various information sources.

6.4.1 #RoboDebt

A variety of URLs were shared in the #RoboDebt dataset. Like hashtags, there is a power-law feature to the sharing of hyperlinks within the tweets in this case, where a small number of information sources were shared by the majority of users, while others were only sparsely shared. In total, 1,131 different domains were shared in 169,770 tweets in the dataset. The domain with the

156 Collective Patterns of Activity highest number of links shared is The Guardian, which is shared in 20,675 distinct tweets. The most widely shared information sources in the dataset are presented in Table 6-8.

Table 6-8: Top information sources in #RoboDebt

Information Source No. of Tweets The Guardian 20,675 Sydney Morning Herald 8,223 ABC News 6,830 Canberra Times 4,845 The Age 2,830 Huffington Post 1,763 News.com.au 1,550 Buzzfeed 1,503 The Saturday Paper 1,408 SBS.com.au 1,281 Crikey News 1,234 With regard to the accounts’ discourse communities and the URLs they shared in the #RoboDebt dataset, there do not seem to be any divergences. In other words, both the Progressive and the Hard-Right clusters relied on, and disseminated the same sources of information in the dataset. This is particularly important when this case is compared with the two other two case studies, where there are striking differences in the sharing of URLs between the antagonistic discourse communities. In the case of the Centrelink controversy, however, most of the top shared URLs link to mainstream and well-established information sources.

Furthermore, some of the information sources in the list of the most-shared domains belong to organisations which are commonly known as having progressive and left-leaning ideologies, such as The Guardian or Buzzfeed

(Hurcombe, Burgess, & Harrington, 2018). Although this is not a conclusive finding per se, its significance became earercl when compared to other case studies. Therefore, I return to this discussion and its implications for this

Collective Patterns of Activity 157 research after presenting the findings ofmost-shared information sources in the other two case studies.

6.4.2 Section 18C

The most widely disseminated URLs in the 18C dataset share similar characteristics with the #RoboDebt case. Among the list of top URLs, mainstream and well-established media outlets such as The Guardian,

News.com.au, and ABC News take a higher position. However, unlike

#RoboDebt, the most frequently shared URLs do not point to a uniform discourse and political orientation. In the #RoboDebt case study, the majority of the media outlets shared by users belonged to the more balanced and, at times left-leaning, sources. By comparison, there is a mix of left- and right- leaning information sources in the 18C dataset. Furthermore, non-news websites also appear high in the list of the top-shared URLs in the dataset. Of course, this is to some extent expected, given that the nature of the two studies is quite different, and that the 18C discussion is a polarising one. Therefore, in a similar way to the discussion of temporal and hashtag metrics, I discuss the different information sources shared by the main antagonistic communities present in the debate (the Progressive and Hard-Right).

Table 6-9: Top information sources in 18C

Domain No. of Tweets theguardian.com 4,278 smh.com.au 2,880 myaccount.news.com.au 2,659 abc.net.au 1,320 theage.com.au 933 theconversation.com 818 sbs.com.au 629 crikey.com.au 591 facebook.com 507 independentaustralia.net 412 buzzfeed.com 349

158 Collective Patterns of Activity humanrights.gov.au 335 corybernardi.com 324 afr.com 318 brisbanetimes.com.au 238 canberratimes.com.au 205 change.org 184 When isolating the top-shared URLs posted by Progressive users from the rest of the dataset, the list becomes more similar to that of the #RoboDebt case study. The most widely shared news source in this regard, The Guardian, has been used in 2,105 tweets by the Progressive community. The next positions in the list are also similar to the #RoboDebt case study, with media outlets such as The Sydney Morning Herald, ABC News, and The Age frequently shared by this community.

Table 6-10: Top information sources for the Progressive cluster in 18C Domain No. of tweets theguardian.com 2,105 smh.com.au 1,116 abc.net.au 382 theage.com.au 352 independentaustralia.net 283 crikey.com.au 242 theconversation.com 204 myaccount.news.com.au 163 sbs.com.au 149 buzzfeed.com 131 afr.com 126 canberratimes.com.au 125 brisbanetimes.com.au 100 newmatilda.com 96 humanrights.gov.au 78 noplaceforsheep.com 75 thenewdaily.com.au 64 On the other hand, The Guardian was only shared in 29 tweets by the Hard-

Right community. The first places in thelist of top information sources by this community are held by media outlets such as News.com.au, The Herald Sun, and The Spectator; these media are generally more read by users on the conservative side of the political spectrum (S. Park, Fisher, Fuller, & Lee,

Collective Patterns of Activity 159 2018). Other than the news and media outlets, the websites of Senator Cory

Bernardi, a conservative Australian politician supportive of repealing Section

18C, and Bernard Gaynor, a far-right campaigner, are also among the list of the top shared URLs by the Hard-Right community.

Table 6-11: Top information sources for Hard-Right cluster in 18C

Domain No. of Tweets myaccount.news.com.au 914 heraldsun.com.au 120 spectator.com.au 90 abc.net.au 84 corybernardi.com 74 change.org 69 dailytelegraph.com.au 65 bernardgaynor.com.au 62 smh.com.au 59 theaustralian.com.au 54 sbs.com.au 48 zanettisview.com 37 theage.com.au 32 skynews.com.au 32 theguardian.com 29 quadrant.org.au 27 9news.com.au 26 2gb.com 24 ipa.org.au 23 The stark differences in the informationsources shared by users in different discourse communities are in line with the findings of other research projects, which have shown how users selectively and strategically choose and share their information sources (Bakshy, Messing, & Adamic, 2015; Garrett, 2009;

Messing & Westwood, 2014). In this sense, the sharing of hyperlinks could also be understood as an articulatory practice, in which one’s discourses are amplified. Collective sharing of URLs, therefore, further helps in the sedimentation of discourses within communities as it provides more visibility and discursive power for these discourses.

160 Collective Patterns of Activity 6.4.3 Immigration

Similar to the 18C case study, aggregate metrics of the most shared URLs in the dataset point to a variety of information sources being shared in the discussions of immigration and asylum policies in the Australian Twittersphere.

While many of the most shared domains in the dataset are related to mainstream and well-established media outlets—such as The Guardian, ABC

News, or SBS.com.au—the presence of some non-mainstream media outlets and information sources—such as Voice of Europe, YouTube, and Breitbart— high on the list necessitated this study to also consider who shares these information sources, in a similar fashion to the investigation of hashtags.

Patterns of URL sharing in the Progressive and Hard-Right communities support the findings of both othercase studies, and findings from the investigation of secondary hashtags. The Progressive discourse community, for instance, extensively relies on mainstream and/or left-leaning media outlets as information sources. Among the list of media outlets shared most by this community are media outlets such as The Guardian, ABC News, SBS.com.au,

New York Times, and Buzzfeed. Most shared URLs to non-media institutions and organisations often link to online petition websites, and support organisations such as Asylum Seeker Resource Centre (asrc.org.au), GetUp.org

(an Australian left-wing political lobbying organisation), or UNICEF.

Table 6-12: Top information sources for the Progressive cluster in Immigration

Domain No. of Tweets theguardian.com 9,529 abc.net.au 1,902 sbs.com.au 1,338 smh.com.au 1,294 action.asrc.org.au 786 nytimes.com 454 getup.org.au 433 myaccount.news.com.au 409

Collective Patterns of Activity 161 asrc.org.au 407 unicef.org.au 376 thesaturdaypaper.com.au 374 radionz.co.nz 366 buzzfeed.com 364 theage.com.au 349 asrccampaigns.nationbuilder.com 340 theconversation.com 329 change.org 319 aljazeera.com 276 independent.co.uk 235 skynews.com.au 227 junkee.com 225 On the other hand, references to mainstream media outlets are less frequent in the discourse of the Hard-Right. Rather, this community relies on ‘alternative’, niche, and right-leaning outlets as information sources. Websites often affiliated with ultra-conservative and right-wing ideologies—such as Breitbart (Benkler,

Faris, Roberts, & Zuckerman, 2017), Voice of Europe (“Media bias/fact check:

Voice of Europe,” n.d.), and Infowars.com (Ramadan & Shantz, 2016)—are in the list of the Hard-Right community’s most shared URLs. Anti-Islam information sources, such as JihadWatch.org and BareNakedIslam.com, are also among the most shared URLs of this community.

Regarding more well-established media outlets, the tendency to rely on the more conservative sources is evident. For instance, where the Progressive community relied on The Guardian as the main information source for news and articles about immigration, this outlet is further down the list of the most shared URLs of the Hard-Right. Meanwhile, media outlets such as The Daily

Mail (UK), The Herald Sun, or Sputnik News are more widely shared. Of course, the presence of mainstream media on the top of both lists could at least partly be due to the fact that mainstream media, on average, publish more articles on a given issue, hence increasing their social media shares.

162 Collective Patterns of Activity Table 6-13: Top information sources for the Hard-Right cluster in Immigration

Domain No. of Tweets voiceofeurope.com 2,052 youtube.com 913 breitbart.com 889 freespeechtime.net 705 dailymail.co.uk 551 jihadwatch.org 426 myaccount.news.com.au 411 barenakedislam.com 392 refugeeresettlementwatch.wordpress.com 258 express.co.uk 218 petertownsend.info 166 infowars.com 144 heraldsun.com.au 127 sputniknews.com 103 news.com.au 101 theguardian.com 101 westmonster.com 100 dailycaller.com 98 abc.net.au 87 foxnews.com 83 zerohedge.com 83 gellerreport.com 82 europereloaded.com 81 6.5 Visibility and Discursive Power

The concept of visibility on social media platforms is a multifaceted and complex notion that could potentially be achieved in different ways. On

Twitter, and in terms of the logics of the platform, a user could be more visible due to having a large number of followers, receiving a high number of retweets and/or @mentions (Riquelme & González-Cantergiani, 2016). However, each of these metrics could, in turn, be due to reasons other than the mere logics of the platform. Although it is possible that a user becomes a ‘Twitter celebrity’, very often, the visibility of an account is also directly tied to its vertical context. Politicians, journalists, media personalities, and the like generally have higher numbers of followers on social media platforms (Choi et al., 2014; Dubois

& Gaffney, 2014).

Collective Patterns of Activity 163 Dubois and Gaffney also argue that opinion leadership and influence are not necessarily the same thing, and that they are highly contextual concepts. They point out that while traditional political players might have a large following

(visibility and opinion leadership), commentators and bloggers are shown to be particularly influential. Furthermore, localised social embeddedness of average users also contributes to their level of influence over their small social circles

(Dubois & Gaffney, 2014).

Others also show that the mere fact of having more followers does not necessarily mean that a user is an opinion leader (Cha et al., 2010). While having more followers can facilitate a model of one-step flow of information from an opinion leader to their follower base, the affordances of social media platforms simultaneously call for two-step and multiple-step flows of information and influence (Hilbert et al., 2016). Similarly, receiving a large number of retweets and/or @mentions is also not an automatic indication of influence or opinion leadership. Rather, the content of the tweet itself also has an impact on its spreadability (Hemsley, 2019). For tweets containing links to information sources, it is shown that the characteristics of the news (e.g. conforming to traditional news values) also play an important role in whether it is further shared by users or not (Trilling, Tolochko, & Burscher, 2017).

Finally, it is also necessary to consider the role of users in accepting another user as an opinion leader (Papacharissi, 2015). That is, an influencer is not simply an opinion leader for all the users on a platform. Rather, there is always an articulatory relation between the opinion leaders and their followers, in which each modifies the identity of the other. Through their active engagement with political topics, curating information, and frequency of Twitter use,

164 Collective Patterns of Activity opinion leaders, in turn, increase the motivation of their followers to actively participate in political processes (C. S. Park, 2013).

In order to unravel the dynamics behind opinion leadership, and the role of

Twitter users in the discursive struggle, antagonism, and agonism, this section focuses on the different metrics of visibility in each case study.

6.5.1 #RoboDebt

6.5.1.1 Visibility in terms of activity

In total, 24,127 distinct accounts are active in the #RoboDebt dataset.

Expectedly, a majority of the accounts posting about this issue (19,719 accounts) posted fewer than 10 tweets, inclusive of retweets. On the other hand, there are also accounts with an exceptionally high level of tweeting about the controversy. The most active account in the dataset, @Asher_Wolf, posted a total of 5,956 tweets in the duration under investigation. A qualitative reading of the Twitter profiles of theseaccounts shows that the most active accounts in the dataset are mainly those with an explicitly activist online persona. These include accounts that self-describe as supporting various causes, such as social justice, equality, environmentalism, truth, and Aboriginal rights.

Table 6-14: Most active accounts in #RoboDebt

Username No. of Tweets @asher_wolf 5,956 @ozequitist 5,512 @otiose94 5,120 @davidtomkins 4,376 @pedwards2014 2,890 @bigvapingnunga 2,571 @chookstweet 2,408 @camybobany 2,197 @centrelink 2,114 @hellbrat 2,093 @info_aus 2,074 @lyndsayfarlow 1,883

Collective Patterns of Activity 165 @virgotweet 1,799 @minhkular 1,792 @wgarnews 1,685 @jpwarren 1,626 @devalara44 1,618 @earthma23 1,551 @firedingo 1,515 @ausunemployment 1,507 In terms of the level of activity, a review of the accounts in the #RoboDebt dataset pointed to the strong presence of a group of accounts that are generally highly involved in a variety of socio-political discussions in the Australian

Twittersphere. These accounts act as curators and agenda-setters in the

#RoboDebt conversations on Twitter, by collecting information from various sources; disseminating news to their followers; actively retweeting each other; and setting the frames for conversations around the issue. In this sense, their high level of activity can potentially provide them with a platform to voice their opinion, as well as a higher chance of being seen by other users.

Within the logics of social media platforms, where there is always a constant sense of competition for engagement and visibility, a higher level of activity can potentially increase the chances of being visible. This is especially true in the case of the most active accounts in this dataset, given that the accounts leading the debate on the Centrelink controversy are not necessarily those with a high status in the vertical context of the debate. These accounts’ discursive power is not rooted in their position in the vertical context. Rather, they strategically take advantage of the affordances of the platform and its logics to gain a higher level of discursive power in the horizontal context, and increase their chances of being perceived as what Papacharissi (2015, p. 46) calls ‘crowd- sourced elites’.

166 Collective Patterns of Activity 6.5.1.2 Visibility in terms of retweets

A review of the most retweeted accounts in the dataset showed different patterns in terms of visibility. @Asher_Wolf stands at the same position with regard to both activity-related and retweet-related visibility. Her tweets have collectively received 19,525 retweets in the dataset. However, other users in the list of most active accounts are not necessarily the most retweeted ones.

Instead, users with a higher position in the vertical context seem to replace them in this regard. The third most retweeted account, for instance, is the

Twitter account of Linda Burney, Member of Parliament for the Australian

Labor Party, with 5,374 retweets.

Table 6-15: Most retweeted accounts in #RoboDebt

Username No. of Received Retweets @asher_wolf 19,525 @not_my_debt 6,361 @lindaburneymp 5,374 @beneltham 5,138 @1petermartin 4,397 @joshbutler 3,582 @noplaceforsheep 3,328 @otiose94 3,307 @jpwarren 3,241 @johnwren1950 3,052 @ozequitist 2,828 @lynlinking 2,709 @sirthomaswynne 2,668 @cpsunion 2,502 @knausc 2,038 @nobby15 1,987 @henry_belot 1,949 @bugwannostra 1,939 @newtonmark 1,901 @dpn78 1,871 In general, the most visible accounts in terms of retweets are often those that are at a higher position in the vertical context of the discussion. For such accounts, their socio-political status, and their access to other forms of

Collective Patterns of Activity 167 visibility, mean that they do not have to rely on the working logics of the platform to gain an audience. Rather, their positions as journalists, politicians, and reporters already provide them with a level of discursive power. This discursive power, which could be seen as a perceived sense of authenticity by other Twitter users, leads to their potential visibility on the platform, in that their tweets are widely retweeted by other users, who can more easily trust the message. This pre-established, vertical discursive position, therefore, gives these accounts a level of visibility on par with, or higher than the more active users. In other words, they can receive as many or more retweets as a highly active user, but with far fewer tweets.

6.5.1.3 Visibility in terms of @mentions

Apart from activity level and retweets, another metric for visibility on Twitter is the number of @mentions received by different accounts. The most

@mentioned accounts in the #RoboDebt dataset show divergent patterns from the two other metrics of visibility discussed above. In this case, the visibility gained by @mentions seems to be also tied to an account’s vertical contextual position, in a similar fashion to the retweet-related visibility. However, due to the different perceived functionalities of retweeting and @mentioning, the most

@mentioned accounts in the dataset are not the same as the most retweeted ones.

Table 6-16: Most @mentioned accounts in #RoboDebt

Username No. of Received @Mentions @centrelink 10,850 @alantudgemp 7,203 @asher_wolf 3,903 @turnbullmalcolm 2,803 @not_my_debt 1,942 @cporterwa 1,754 @lindaburneymp 1,220 @ozequitist 1,172

168 Collective Patterns of Activity @smh 994 @jpwarren 891 @abcnews 865 @guardian 779 @sussanley 698 @randlight 680 @otiose94 660 @1petermartin 641 @beneltham 626 @hankjongen 614 @yathinkn 609 @billshortenmp 601 A review of the most @mentioned accounts in the dataset showed that the accounts with the highest level of visibility can be roughly categorised into two main groups. The first group of most @mentioned accounts consists of those who are @mentioned due to their socio-political status, in that they are collectively addressed by other accounts in either a negative way (as a means of criticism), or to call them to take action on an issue. As a case in point, the

Twitter account of Alan Tudge, or of the then Prime Minister Malcolm

Turnbull, are among the group of accounts mostly @mentioned due to their role in the debate, or their political position. The second group in this categorisation are those accounts that are @mentioned in order to draw their attention to a topic, issue, or argument, or to comment on what they said about an issue. News outlets such as The Sydney Morning Herald, The

Guardian, and ABC News, or prominent journalists and reporters such as Ben

Eltham or Peter Martin, are among such accounts.

Of course, this categorisation does not necessarily apply to all @mentioned accounts, and cannot completely explain all the dynamics of @mentioning patterns on Twitter. For instance, the high level of @mentions received by some of the Twitter activists could partly be due to the mutual @mentioning among activists themselves, who aim to amplify their visibility (Hilbert et al.,

2016). Furthermore, it is not possible to provide a satisfactory categorisation

Collective Patterns of Activity 169 without an in-depth qualitative investigation of the tweets in which different accounts are @mentioned. Rather, this grouping of users merely serves as an initial sense-making step, in which different metrics of visibility can be compared. Further investigation of @mentioning dynamics (Section 7.5) and the qualitative analysis of tweets (Chapter 8) can shed more light in this regard.

What is clearer from the list of the most @mentioned accounts, however, is that such accounts are mainly those with a higher socio-political status in the vertical context of the discussion. Also, especially in the case of politicians and journalists, they are the accounts with the necessary power to cause change— either through changes in policy, or by raising awareness and giving voice to those influenced by the algorithmic debt notices. In this sense, the act of

@mentioning can be perceived as a discursive strategy, demarcating one’s position vis-à-vis vertical and horizontal social actors.

In comparison to retweeting patterns, one can observe how users collectively retweet those with similar discursive positions to endorse their message and amplify their voices, or @mention them when inviting them to see a particular point and discuss it, to further amplify their discursive position. On the other hand, other users are @mentioned to be criticised, or pressured into taking action and addressing the issue by changing policies or stopping the algorithmic data-matching program. These different dynamics point to a potential for what

I refer to as ‘horizontal/vertical agonistic/antagonistic practices’ (Section 3.5).

The metrics discussed in this section, however, could not alone provide this study with the information required to make such conclusions. It was necessary to compare this case with the other case studies in the project, to investigate the collective patterns of interactions and articulatory practices formed due to

170 Collective Patterns of Activity Twitter affordances, and to delve more deeply into the discourses shared by the publics and collectives involved in the various debates. In the following sections of this chapter, I make this comparison of the case studies. The next two chapters then explore the dynamics of the networks and discourses involved in the three case studies.

6.5.2 Section 18C

6.5.2.1 Visibility in terms of activity

In terms of the level of activity, the list of most active users in the 18C case is more evenly distributed than in the #RoboDebt case study. This potentially indicates that unlike the #RoboDebt issue, discussions of Section 18C are not necessarily curated or channelled by certain users. Rather, they arise more organically, due to users’ interest and embeddedness in political debates.

Qualitative examination of the Twitter profiles of themost active users in the dataset also pointed to the presence of actors with varied discourses within the debate. Following the findings of the previous steps on polarisation tendencies within the 18C dataset, I also compared the most active users in the two highly active antagonistic communities involved in the debate.

Table 6-17: Most active accounts in 18C

Username No. of Tweets @otiose94 564 @rjstrikers 498 @minhkular 483 @sirthomaswynne 432 @geoffrey_payne 425 @pedwards2014 373 @mickkime 362 @frank8427zz9za 359 @daveyk317 344 @broomstick33 334 @mariamatthews5 331 @devalara44 324 @etalbert 320

Collective Patterns of Activity 171 @australian 291 @ochreblue 289 @justanotweet 286 @msmwatchdog2013 285 @tinalorentz 279 @lelhulagirl63 277 @frygerard 273 Overall, the level of activity between the Progressive and Hard-Right communities does not differ greatly. The most active Progressive account in the dataset has 498 tweets on the issue, while for the Hard-Right, it is 359 tweets. However, in contrast to the #RoboDebt case study, there are big differences between both the overall number of tweets and the level of activity of the most active tweeters. That is, the average number of tweets per capita is higher in the #RoboDebt case study than in 18C (18.24 tweets per account versus 7.22, respectively). Additionally, compared to #RoboDebt, where the most active accounts each posted over 1,500 tweets over the course of six months, the level of activity in the 18C case is far less, with no account posting over 500 tweets in a span of more than a year. This, as discussed in Section

5.2.1, could be an indication of a sense of perceived acuteness in the

#RoboDebt case, while the debate over 18C is not necessarily perceived so.

Table 6-18: Most active Hard-Right and Progressive accounts in 18C

Hard-Right Progressive Username No. of Tweets Username No. of Tweets @frank8427zz9za 359 @otiose94 564 @gato188 242 @rjstrikers 498 @shockresistant2 195 @minhkular 483 @gerster_kaylene 191 @sirthomaswynne 432 @margi1959 189 @geoffrey_payne 425 @matthewhayden 188 @pedwards2014 373 @monster_dome 167 @mickkime 362 @auntyneville664 165 @daveyk317 344 @w0tn0t 161 @broomstick33 334 @j1m1v 161 @ochreblue 289 @den2114 145 @justanotweet 286 @ronagitsham 138 @msmwatchdog2013 285 @roulereport 121 @tinalorentz 279

172 Collective Patterns of Activity @peterwhill1 120 @frygerard 273 @taxenuffalready 114 @fehowarth 271 @petefromhaynsw 113 @3wombats 271 @garthgodsman 110 @davidbewart 261 @billdobell 110 @talaolp 225 @telesynth_hot 107 @glebbern 222 @cinebar2o 106 @opa1420 219 6.5.2.2 Visibility in terms of retweets

Considering visibility and discursive power in terms of retweets, the most retweeted users in this case exhibit the same patterns as in the #RoboDebt case. That is, accounts receiving the highest number of retweets in the list are generally those in a higher position within the vertical context of the discussion.

Media personalities, public figures, nda journalists—such as Mark Di Stefano

(Buzzfeed), Mike Carlton (media commentator), Alice Workman (Buzzfeed), and Tony Martin (comedian)—are among the list of most retweeted accounts.

Furthermore, political figures tweeting about the issue have also received a large number of retweets.

Table 6-19: Most retweeted accounts in 18C

Username No. of Received Retweets @markdistef 2,752 @mikecarlton01 2,715 @markdreyfusqcmp 2,392 @mariamveiszadeh 1,728 @sirthomaswynne 1,251 @mrtonymartin 1,197 @workmanalice 1,112 @kon__k 937 @skynewsaust 869 @bernardkeane 846 @gabriellechan 822 @murraywatt 775 @mslou27 765 @timsout 746 @joshbutler 716 @johnwren1950 701 @vanbadham 697 @bugwannostra 693 @joshgnosis 691

Collective Patterns of Activity 173 @davidcampbell73 689 However, considering the polarised debate over 18C, and the fact that the discussions in this regard are not curated or guided by particular ‘influencers’, it is useful to also examine whether or not the aforementioned list of public figures with the highest number of retweets are retweeted by all the users in the dataset. It is likely that in the competition over gaining visibility and becoming hegemonic, different communities retweet users with similar discourses, in order to gain the upper hand in the discussion. Therefore, in a similar fashion to the previous analytical steps, I separately examined the most retweeted accounts in each antagonistic cluster in the debate.

Separating the communities, it became clearer that the list of the most retweeted accounts is heavily influenced by the number of users in the

Progressive cluster. As I pointed out in Section 6.2.2, the Progressive cluster in the Australian Twittersphere is approximately three times larger than the

Hard-Right cluster. Users such as Alice Workman and Mark Di Stefano, who are affiliated with media outlets with an explicitly progressive and left-leaning positionality such as Buzzfeed Australia (Hurcombe et al., 2018), are almost exclusively retweeted by users in the Progressive discourse community. On the other hand, the Hard-Right community strategically retweets their conservative counterparts, such as Rita Panahi (conservative journalist),

Zanetti Cartoons (conservative cartoonist), The Australian, and Cory Bernardi

(conservative politician).

In this light, the articulatory practice of retweeting, in which one’s discourse and identity is constantly negotiated and modified by the accounts one retweets, relies both on the discursive, strategic choices of the user doing the act of retweeting, and at the same time, on the structural affordances of the

174 Collective Patterns of Activity platform and its technological design. Without the possibility of retweeting, or the affordance of forming follower/followee relationships on Twitter, this articulation would not be possible in its current form.

Table 6-20: Accounts receiving the highest retweets from the Hard-Right and Progressive clusters in 18C

Hard-Right Progressive Username No. of Retweets Username No. of Retweets @ritapanahi 329 @mikecarlton01 1,321 @zanetticartoons 235 @markdreyfusqcmp 1,218 @australian 229 @markdistef 1,077 @skynewsaust 205 @sirthomaswynne 743 @corybernardi 196 @mariamveiszadeh 554 @chriskkenny 181 @workmanalice 467 @teamtabbott 174 @bugwannostra 435 @paulinehansonoz 151 @gabriellechan 434 @frank8427zz9za 134 @murraywatt 402 @omgthemess 117 @johnwren1950 396 @alanthorold 117 @joshbutler 388 @bernardgaynor 102 @bernardkeane 377 @monster_dome 97 @kon__k 365 @senatormroberts 92 @vanbadham 330 @rowandean 82 @kieragorden 328 @pruemacsween 82 @mrtonymartin 327 @lozzacash 78 @mana_kailani 309 @jkalbrechtsen 77 @davrosz 294 @theipa 76 @mslou27 288 @timwilsoncomau 73 @eddyjokovich 288 6.5.2.3 Visibility in terms of @mentions

Finally, moving to the metrics of visibility in terms of @mentioning, similar patterns to those in the #RoboDebt case study emerge. The most @mentioned account in the dataset, for instance, is the Twitter account of the then Prime

Minister, Malcolm Turnbull. Following that, media outlets Sky News Australia and The Australian received the highest number of @mentions. The other accounts in the list of top @mentioned accounts also follow the same patterns as those in the #RoboDebt case study, where accounts with a higher level of vertical discursive power received the highest number of @mentions. This was due either to their political position (e.g. politicians), or their role in

Collective Patterns of Activity 175 channelling conversations and disseminating information (e.g. journalists and media outlets).

Table 6-21: Most @mentioned accounts in 18C

Username No. of Received @Mentions @turnbullmalcolm 4,702 @skynewsaust 1,302 @australian 1,234 @corybernardi 1,197 @chriskkenny 848 @ritapanahi 842 @timsout 836 @cathywilcox1 771 @markdistef 708 @davidcampbell73 689 @davidleyonhjelm 688 @johnwren1950 674 @abcnews 642 @aushumanrights 638 @paulinehansonoz 632 @nacchoaustralia 631 @smh 627 @gilliantriggs 556 @nick_xenophon 541 @billshortenmp 520 I argued in Section 3.6 how the practice of @mentioning could be understood as a discursive strategy, directly related to one’s symbolic resources and discourses. Users @mention others to achieve different communicative and discursive goals; for example, calling them to action, bringing their attention to something, talking to/at/about them, and criticising them. This is also evident in the accounts @mentioned by users from different discourse communities in the Australian Twittersphere. Among the most @mentioned accounts by the Progressive community, for example, there is a mix of both progressive (e.g. cartoonist Cathy Wilcox, Mark Di Stefano, and Mark Carlton) and conservative (e.g. Cory Bernardi, The Australian, and Pauline Hanson) social actors.

176 Collective Patterns of Activity This points to the different discursive meanings ascribed to the act of

@mentioning, in that one might @mention a discursively resonant actor for positive purposes—such as giving them visibility, or bringing their attention to something; on the other hand, one might @mention a discursively dissonant actor for purposes of negation, such as criticism.

Table 6-22: Accounts receiving the most @mentions by the Progressive cluster in 18C

Username No. of Received @Mentions @turnbullmalcolm 1,832 @johnwren1950 359 @corybernardi 337 @independentaus 312 @australian 311 @skynewsaust 247 @cathywilcox1 246 @markdistef 239 @theipa 222 @smh 202 @davidcampbell73 201 @murpharoo 201 @nick_xenophon 192 @markdreyfusqcmp 191 @abcnews 177 @paulinehansonoz 172 @financialreview 171 @novaperis 162 @kieragorden 153 @mikecarlton01 151 The same mixture of discursively resonant and dissonant actors is seen in the accounts @mentioned by the Hard-Right cluster. Conservative actors such as

Rita Panahi (journalist), Sky News, and Cory Bernardi are among the most

@mentioned users in this community. However, progressive actors such as

Australia’s Race Discrimination Commissioner, Tim Soutphommasane are also on the list.

Table 6-23: Accounts receiving the most @mentions by the Hard-Right cluster in 18C

Username No. of Received @Mentions @turnbullmalcolm 556

Collective Patterns of Activity 177 @ritapanahi 314 @skynewsaust 289 @chriskkenny 255 @corybernardi 249 @timsout 229 @australian 189 @tonyabbottmhr 183 @gilliantriggs 173 @teamtabbott 172 @davidleyonhjelm 165 @zanetticartoons 165 @rowandean 138 @paulinehansonoz 137 @alanthorold 127 @billshortenmp 126 @aushumanrights 107 @liberalaus 92 @3aw693 90 @mirandadevine 85 In this light, the articulatory nature of @mentioning on Twitter becomes clearer. Each antagonistic community within the debate calls their own opinion leaders to attention, criticises the opinion leaders of their opponents, and amplifies the voices of their own discourse. The question which remains in this regard, then, is whether the affordances of Twitter, and the articulatory practices resulting from them, allow for any type of agonistic opportunity, as opposed to mere antagonism. The answer to this question cannot solely rely on the metrics discussed above. In the next two chapters, therefore, I move more deeply into the networks and symbolic resources of this antagonistic space to look for possibilities and conditions of agonism and agonistic practices.

6.5.3 Immigration

6.5.3.1 Visibility in terms of activity

Similar to both the RoboDebt and 18C case studies, the majority of users in the Immigration dataset posted only a handful of tweets during the course of the debate. A number of accounts, however, show a high level of activity. A

178 Collective Patterns of Activity review of the most active accounts in the dataset showed that some ‘crowd- sourced elites’ (Papacharissi, 2015) lead the conversations through actively curating information, tweeting about the issue, and engaging with other users.

Also, qualitative examination of the Twitter profiles of these active users pointed to the presence of different discourses in the debate.

Following the procedure used in the examination of hashtags and URLs, I separated the most active accounts in each of the major antagonistic discourse communities to identify the actors with the highest level of visibility in terms of tweeting activity. Although the Progressive and Hard-Right communities are not the only highly active clusters discussing the immigration debate, they are the most active ones. In this section, I only review these two communities; in the next stages of analysis (discussed in the next two chapters), however, I incorporate the other involved clusters.

Table 6-24: Most active accounts in Immigration

Username No. of Tweets @juliet777777 7,818 @graffitiexpert 5,589 @amandaperram 4,926 @riserefugee 4,245 @jsalmonupstream 3,451 @fridaygirl13 3,068 @rjstrikers 2,884 @blanketcrap 2,811 @prufrockery 2,665 @jouljet 2,473 @catherinemaths 2,473 @simon_a_wood 2,133 @cunningham_cch 2,130 @tomwoods55 2,079 @rranwa 2,027 @somuchbullsh 1,955 @marilynshephe15 1,775 @emlafudd 1,750 @skinnergj 1,738 @opa1420 1,735

Collective Patterns of Activity 179 The most active account in the Progressive discourse community has a total of 5,589 tweets in the dataset. This highly active account, seemingly set up to support refugee rights on Manus and Nauru, however, had fewer than 1,000 followers as of February 2019, unlike the most active account in the

#RoboDebt case study (@Asher_Wolf), which had over 65,000 followers. This potentially enforces the argument (in the introduction to this section) that a high level of activity by itself is not necessarily equal to a high level of influence.

The next highly active account, with 4,245 tweets and 9,089 followers as of

February 2019, belonged to RISE (@riserefugee), which describes itself as “the first welfare and advocacy organisationgoverned entirely by ex-detainees, asylum seekers, and refugees in Australia” (“RISE,” n.d.). Among the list of most active accounts, there are several with similar self-descriptions, explicitly presenting themselves as refugee supporters. At the time of examining the

Twitter profiles of these accounts, many of them used a profile picture promoting the slogan “Blue for Nauru”, an online campaign to support children in the Nauru detention centre. Support is shown by putting a blue layer on top of online profile pictures.

Table 6-25: Most active Progressive accounts in Immigration

Username No. of Tweets @graffitiexpert 5,589 @riserefugee 4,245 @fridaygirl13 3,068 @rjstrikers 2,884 @blanketcrap 2,811 @prufrockery 2,665 @simon_a_wood 2,133 @cunningham_cch 2,130 @rranwa 2,027 @somuchbullsh 1,955 @marilynshephe15 1,775 @opa1420 1,735 @msmwatchdog2013 1,684 @mdmabsentminded 1,675

180 Collective Patterns of Activity @3wombats 1,589 @suthernx 1,570 @chiloutrevived 1,549 @deniseshrivell 1,500 @pckj3627 1,446 @otiose94 1,378 The most active accounts in the Hard-Right cluster, however, display very different profile descriptions. Severalaccounts in the list self-describe with words and phrases related to the American and international far-right discourses, nationalism, and conservativism; for example, ‘seeking truth’,

‘supporting veterans’, ‘anti-Islam’, ‘MAGA’ (Make America Great Again, the slogan used by Donald Trump), and ‘proud Australian’. Additionally, many include links to their profiles in Gab.ai, a platform known as a far-right equivalent of Twitter (Zannettou et al., 2018). Another interesting pattern in this regard is that as of February 2019, Twitter had suspended at least 5

Twitter profiles of the top 20 most active accounts in this community; this could indicate their frequent violations of Twitter’s terms of service.

Table 6-26: Most active Hard-Right accounts in Immigration

Username No. of Tweets @juliet777777 7,818 @donadeedooda 1,728 @saveaustralia1 1,323 @eliseulascado 1,084 @nobby_greens 833 @louiserozema 766 @bruhnrose 750 @gerster_kaylene 707 @thelamarckian 689 @den2114 655 @stevew2308 608 @laborfail 597 @dockendoris 548 @johnricho45 520 @havenaar64 518 @sunilkumaraus 488 @826maureen 482 @53pamela 478

Collective Patterns of Activity 181 @zakford01 462 @mortimershole 451 6.5.3.2 Visibility in terms of retweets

With regard to visibility as a result of retweets, the patterns are very similar to the 18C case study. Again, the most retweeted accounts in this regard are generally public figures, activists, journalists, and politicians. Similar to the

18C case, but unlike #RoboDebt, different actors and discourses are present in the top retweeted accounts. Therefore, it is important to consider the most retweeted accounts in the major discourse communities involved in the debate.

Table 6-27: Most retweeted accounts in Immigration

Username No. of Received Retweets @kon__k 26,710 @behrouzboochani 18,290 @julianburnside 13,662 @sarahrubywrites 11,871 @riserefugee 7,067 @v_of_europe 6,111 @frbower 5,331 @deniseshrivell 4,607 @refugees 4,238 @noplaceforsheep 3,791 @simonahac 3,682 @amandaperram 3,681 @aussie4refugees 3,443 @bendohertycorro 3,375 @abdulaziz_ada 3,362 @sbsnews 3,187 @asrc1 3,061 @prisonplanet 3,035 @johnwren1950 3,010 @joshbutler 2,909 In a similar way to the 18C case, each community endorses the messages of different users through retweeting. For the Progressive discourse community, for instance, the most retweeted user is Kon Karapangiotidis, the CEO and founder of the Asylum Seeker Resource Centre (ASRC), whose tweets were retweeted 8,836 times by the Progressive cluster. Following Karapangiotidis,

182 Collective Patterns of Activity Behrouz Boochani’s messages are the most widely disseminated, with 5,731 retweets. Boochani, an Iranian Kurd journalist, is one of the most well-known residents of the Manus offshore detention centre. Given the lack of access by journalists to the detention centres, his active tweeting from the island has become a source of information about the situation in the detention centres.

Other widely retweeted accounts by the Progressive community include Julian

Burnside (Australian barrister and refugee rights advocate), RISE, and other refugee rights activists.

Table 6-28: Accounts receiving the most retweets by the Progressive cluster in Immigration

Username No. of Received Retweets @kon__k 8,836 @behrouzboochani 5,731 @julianburnside 5,108 @sarahrubywrites 5,083 @riserefugee 4,693 @aussie4refugees 3,399 @deniseshrivell 2,492 @frbower 1,894 @noplaceforsheep 1,731 @amandaperram 1,700 @johnwren1950 1,470 @abdulaziz_ada 1,422 @simonahac 1,408 @barnsgreg 1,255 @susanamet 1,255 @phbarratt 1,238 @littlebertie01 1,169 @peterwmurphy1 1,162 @bendohertycorro 1,126 @racvictoria 1,121 In the Hard-Right cluster, however, the most retweeted accounts are generally public figures, institutions, and politicians known for their far-right and/or ultra-conservative discourses. Voice of Europe, with 2,345 retweets, is the most retweeted account in this community. Other widely retweeted accounts in this cluster include conservative and (far-) right accounts, and websites such as

AuPolNews.com (a far-right and anti-Islam site), Paul Joseph Watson

Collective Patterns of Activity 183 (conspiracy theorist and far-right YouTube personality), and Pauline Hanson

(conservative Australian politician).

Table 6-29: Accounts receiving the most retweets by the Hard-Right cluster in Immigration

Username No. of Received Retweets @v_of_europe 2,345 @juliet777777 2,309 @aupolnews 1,065 @prisonplanet 787 @paulinehansonoz 684 @amike4761 641 @skynewsaust 574 @petersweden7 494 @markacollett 467 @westmonsteruk 426 @amymek 411 @realdonaldtrump 408 @teamtabbott 391 @ozcrimenews 389 @realmarklatham 374 @youtube 371 @kthopkins 342 @charliekirk11 329 @den2114 320 @mailonline 313 6.5.3.3 Visibility in terms of @mentions

In terms of visibility as the result of @mentions, more similarities can be seen between the two antagonistic communities. The most @mentioned account by both the Progressive and the Hard-Right clusters is Peter Dutton, the then

Australian minister of Immigration. As shown in previous sections, Dutton was the centre of attention for a number of reasons, including his political position as the Immigration Minister, his controversial position and comments about refugees and asylum seekers, his comments about accepting white South

African farmers into Australia, and controversy over his abuse of ministerial powers in the case of the two au pair visas. Malcolm Turnbull, the then Prime

184 Collective Patterns of Activity Minister, is also a highly @mentioned account by both communities. Other similarities between the @mentioning patterns of the two antagonistic communities are the visibility of news outlets such as ABC News and Sky News

Australia. However, their position in the list is slightly different.

Table 6-30: Most @mentioned accounts in Immigration

Username No. of Received @Mentions @peterdutton_mp 20,799 @turnbullmalcolm 11,718 @realdonaldtrump 6,555 @asrc1 6,416 @billshortenmp 5,490 @abcnews 5,311 @scottmorrisonmp 5,168 @behrouzboochani 4,402 @skynewsaust 3,578 @julianburnside 3,505 @gedkearney 2,581 @australianlabor 2,486 @kon__k 2,292 @refugees 1,944 @sarahrubywrites 1,836 @liberalaus 1,831 @barriecassidy 1,801 @tonyabbottmhr 1,699 @murpharoo 1,670 @qanda 1,509 The divergent patterns of @mentioning between the two antagonistic communities are also significant in terms of the actors referred to, addressed, or discussed by these discourses. The Progressive discourse community, for instance, widely @mentioned the ASRC (2,245 mentions) and ABC News

(1,983 mentions). Bill Shorten, as the then leader of the Australian Labor

Party, is also in the list of the most @mentioned accounts in the Progressive community. Finally, this community also actively @mentions other figures related to the refugees and asylum seekers, such as Behrouz Boochani and

Julian Burnside. On the other hand, the Hard-Right community engages with

Collective Patterns of Activity 185 other figures and organisations, such as Donald Trump, Sky News, Cory

Bernardi, and Pauline Hanson.

Table 6-31: Accounts receiving the most @mentions by the Progressive and Hard-Right clusters in Immigration

Progressive Hard-Right Username No. of @Mentions Username No. of @Mentions @peterdutton_mp 7,631 @peterdutton_mp 776 @turnbullmalcolm 4,530 @realdonaldtrump 578 @asrc1 2,245 @skynewsaust 459 @abcnews 1,983 @turnbullmalcolm 433 @billshortenmp 1,961 @tonyabbottmhr 353 @scottmorrisonmp 1,756 @paulinehansonoz 253 @behrouzboochani 1,490 @isupport_israel 248 @julianburnside 1,224 @julianburnside 231 @gedkearney 1,211 @scottmorrisonmp 215 @australianlabor 1,045 @abcnews 210 @skynewsaust 1,015 @chriskkenny 205 @sarahrubywrites 925 @aupolnews 193 @realdonaldtrump 872 @liberalaus 184 @kon__k 847 @corybernardi 180 @murpharoo 754 @billshortenmp 175 @liberalaus 595 @benelongtime 171 @randlight 555 @saveaustralia1 146 @nickmckim 512 @australian 144 @deniseshrivell 511 @refugeewatcher 138 @helprefugeesoz 486 @potus 133 6.6 Conclusions

In this chapter, I examined the datasets using various metrics of communication to gain an exploratory understanding of the aggregate and collective dynamics of the discursive struggle in the three case studies that form this research project. In particular, I investigated the tweeting patterns over time, the use of hashtags, the sharing patterns of information sources, and the different metrics of visibility, such as activity levels, retweeting patterns, and @mentioning patterns.

186 Collective Patterns of Activity Overall, the temporal patterns of tweeting activity in all three cases show that the volume of activity of users is directly related to the evolution of the discourse in the broader socio-political context of the debates and discussions.

Twitter users react to the various events and news in the broader context by sharing information on the platform, voicing their opinion about the event, endorsing each other’s messages through retweets, or bringing other social actors into the conversations by @mentioning them. However, at the same time, this general pattern is influenced by the discourses that form Twitter users’ perspectives. That is, although the overall pattern of tweets over time is generally a reflection of how the discussion unfolds in the socio-political context, users strategically react to different contextual triggers based on the discourses in which they are positioned.

In this sense, the act of tweeting per se can be understood as an articulatory practice, where there is always a dialectical relationship between the triggering event and the user reacting to it. In this two-way relationship, the event—and along with it the discourses, actors, and materialities—and the user tweeting in reaction to the event, are moments in a contingent, articulatory relation, in which the user’s identity is constantly negotiated in relation to the event(s) they react to. At the same time, collective reactions of the users to certain events (who reacts to what, and how) constantly negotiate the boundaries of the discursive field in which the event is positioned.

With regard to the use of hashtags, I showed how different discourses employ hashtags in a strategic fashion to perform different discursive strategies. As a material affordance of Twitter, a hashtagenables users to make their tweet visible to potentially everyone on the platform; to mark its topicality; to interdiscursively invoke, reproduce, and modify a discourse in relation to other

Collective Patterns of Activity 187 discourses; or to identify the discursive formations in which they are operating.

This simple affordance, then, can at the same time be conceived as a discursive strategy, an articulatory practice, a communication function, and more.

The articulations created by the relation between a Twitter user, their tweets, and the hashtags they use, constantly negotiate the identity of the user and the discourses within which the hashtag, the tweet, and the user are articulated. Collective use of hashtags by more users, in effect, leads to the accumulation of visibility of that hashtag, and with it, the discourse of the collective users; this, in turn, facilitates an accumulation of power for the discourse in its struggle to become hegemonic, to sediment its symbolic resources, and to attain closure.

The strategic collective use of hashtags is also directly influenced by both the horizontal logics of the platform, and the vertical logics of the context. The more users incorporate a hashtag in their tweets, the more likely that hashtag will find its way into the ‘trending topics’ section of Twitter, be discovered and discussed by media outlets, and enter the broader vertical context. As shown in the analysis of temporal tweeting activities, the vertical context of the discussion, including media outlets, plays an important role in the volume of tweets. Therefore, once a hashtag finds its way out of the horizontal context of the platform and into the vertical context, it can gain more visibility, and with it, more power and likelihood of hegemony for the discourse. This circular relationship between the collective activities of users, the material affordances of a platform, and the broader discursive environment, constantly reconfigures and modifies the discoursescirculating in society.

The same articulatory relation can be envisaged for the use of URLs. I showed how users collectively share URLs that often amplify their discourses. This

188 Collective Patterns of Activity practice creates an articulation between the information source and the user, in a similar way to the way hashtags are used. Sharing hyperlinks to certain sources, and avoiding the dissemination of others, constantly negotiates, amplifies, and modifies the discourses and identities of the user. At the same time, however, in the competitive environment of media outlets, where each organisation is in a constant struggle to be heard, read, and distributed, the identity of the outlet is always contingent upon by whom, how much, and how it is read.

Simply put, without an audience willing to read and share an outlet’s information, the outlet will lose its chances of survival. In this sense, collective sharing of URLs to a media outlet not only amplifies the discourse of the media and the user, but also constantly modifies this discourse by indicating to the outlet what users are more/less willing to share and read, and how many users will do so. This, in turn, leads to the media responding by the production of similar discourses, and further sedimenting the discourse.

Finally, this chapter also examined the different metrics of visibility of users on Twitter. Separately investigating each metric (i.e. level of activity, retweets, and @mentions), I showed how each of these affordances can achieve different discursive strategies. Furthermore, the analyses provided in this chapter show how users strategically select who to retweet and/or @mention. In this regard, a finding from this stage of the analysis is the inherent differences between visibility and discursive power. Although visibility on Twitter can be achieved by different affordances—such as having more followers, being more active, using (proper) hashtags, incorporating images and videos in tweets, being retweeted more, or being @mentioned more—these affordances and metrics do not necessarily achieve the same functions in terms of discursive dynamics. The

Collective Patterns of Activity 189 comparisons between active users in the three case studies, for instance, show how users follow the logics of the platform to gain a higher level of visibility.

In this regard, the general assumption is that given the same number of followers, a more active user has a higher chance of being seen; this could then lead to being retweeted, followed more, and so forth.

At the same time, however, I showed how the most active users in each dataset are often motivated to do so by reasons other than simply the will to be seen.

Rather, the will to be seen is always triggered by the discursive struggle, in a constant battle to become hegemonic. In this sense, a highly active user is not merely promoting themselves to become a ‘Twitter celebrity’. Rather, the user and their tweets are representative of the discourse they are amplifying. This becomes clearer when comparing the highly active users with the highly retweeted ones.

The three case studies show how users often retweet those whose messages resonate with their own discourses. At the same time, the discursive resonance is always accompanied by a sense of credibility, in that the most retweeted users are generally those who already hold a higher level of discursive power, or as I conceptualised, are higher in the vertical context of the discursive environment.

In some cases, however, these discursively powerful individuals—‘crowd- sourced elites’, as Papacharissi (2015) calls them—are chosen by other users.

Even in such cases, however, the opinion leaders of each discourse, whether they are the vertically powerful or the horizontally chosen elites, are strategically and collectively selected by users based on the discursive resonance of their messages. As Carpentier explains, users are constantly avoiding “discursive dissonance” (Carpentier, 2017, p. 30) by only relying on

190 Collective Patterns of Activity opinion leaders who amplify their already-held beliefs and discourses.

Therefore, the act of retweeting also becomes an articulatory practice, in which the identity of both the retweeter and the retweeted user is modified. The users, then, become moments within the discursive network, collectively working in the hegemonic process.

The same articulatory relation can be conceptualised for the practice of

@mentioning, and the dynamics lying behind the collective perception of what an @mention can achieve. As the analyses of the three case studies show, the collective and strategic @mentioning of other users often situates them in relation to the discourses involved in a debate. @Mentioning users high in the vertical context, for instance, is often due to their perceived role in the broader discursive environment. In all three case studies, the list of users with the highest number of @mentions often include the key politicians in the debate.

Additionally, when separating the users based on their discourse communities,

I showed how this list takes new forms, in that the users @mentioned frequently by different communities often reflect the community’s perception of the role and significance of the actor in the discursive environment. In some cases, a collective of users @mentions an account to criticise them, attack them, or call them to action. In other cases, the collective @mentioning of a user is to give them more visibility, since their messages are perceived to resonate with the collective’s discourses.

The complexities involved in the affordance of @mentioning, and the various discursive functions it can achieve, do not allow this chapter to make more in- depth conclusions about why certain accounts are @mentioned more by certain communities. This requires a more in-depth and qualitative analysis of the tweets containing the @mentions also, and this is provided in Chapter 8.

Collective Patterns of Activity 191 Regardless, an exploratory reading of the Twitter profiles of the highly

@mentioned accounts in each case study showed the role of discourses and discursive struggles in the practice of collective @mentioning, where an articulatory relation is always present between the mentioner and the mentioned, and where the identity of each is contingent on, and modified by the other. This modification, however, could be based on discursive resonance or dissonance. Mere aggregate metrics, such as the ones used in this chapter, however, cannot satisfactorily show which is the case. Therefore, I pause this discussion here, and return to it in Chapters 7 and 8.

The findings of this stage of analysis, and the above discussion and interpretation of these findings, point to the presence of different discursive fields for each case study. I showed howthe examination of the different metrics provided this study with a good grounding in the collective patterns and broader picture of the discursive environment. However, the question that this stage of the study cannot answer is whether the present collectives and communities are simply screaming into a void, or if there is any form of interaction. Additionally, if there is a form of interaction present, the question then relates to the dynamics involved, and whether the interaction is purely antagonistic and inimical, or whether there is any space for agonistic practices.

Finally, if there is an agonistic space, then it is significant to investigate its dynamics, in order to get closer to the broadest question of this research: the inter-relationship of social media platforms and democracy. In order to answer such questions, this study now investigates the dynamics of the networks created as the result of collective articulatory practices such as retweeting and

@mentioning.

192 Collective Patterns of Activity 7 Discursive Networks of Articulation

For both parties in a controversy, the most disagreeable way of retaliating is to be vexed and silent; for the aggressor usually regards the silence as a sign of contempt. (Nietzsche, 2006, p. 159)

7.1 Introduction

In Chapter 6, I examined the aggregate and collective metrics of communications in the Australian Twittersphere. The findings show how the visibility of different actors is often a result of their status in the vertical context of the debates, and in some cases, is due to their perceived position with regard to the topic of the discussion; the latter determines their status in the conversations as ‘crowd-sourced elites’ (Papacharissi, 2015).

Aggregate metrics used in the previous chapter, however, could not satisfactorily provide information about who retweets/@mentions whom. This differentiation is important since mere aggregates cannot necessarily show the potential reach and impact of a given account in the broader discussion.

Additionally, a question left unanswered in the previous chapter is related to the groups of users often retweeted/@mentioned by the same accounts. In other words, it is not clear yet whether a highly retweeted account in the network is retweeted by all the users in the network, or by only a few. Also, it is not yet clear whether the same users who retweet/@mention a particular account also retweet/@mention any other accounts. Answers to such questions provide this study with information about the dynamics of discursive struggles, by

Discursive Networks of Articulation 193 pinpointing the dynamics of information flows within, between, and among communities of users (intra-, inter-, and extra-cluster dynamics).

To this end, it is necessary to first identify communities of users that are created due to the collective use of the affordances of Twitter, such as retweets and @mentions. In this chapter, therefore, I examine the network dynamics of the case studies, and investigate the communities in each, and the dynamics of interactions among them. The first half of this chapter is dedicated to the exploration of the dynamics of retweeting in each case study. I then move to examine the networks of @mentions.

7.2 Retweet Networks

Fundamentally, the act of retweeting on Twitter makes a tweet, and the user posting it, potentially visible to one’s followers. In this basic understanding of the practice of retweeting, any retweet is inherently an indication of the information or users that the retweeter deems worthy of dissemination.

Although different users might have different motives for retweeting a certain tweet (boyd, Golder, & Lotan, 2010), retweeting eventually leads to an increase in the potential visibility of the tweet and its original poster. In this sense, whether the retweeting is performed to bring attention to a message one agrees or disagrees with, the very act of retweeting, in effect, increases the visibility of the tweet, and is a form of amplification and publicity. This brings to mind the public relations adage that ‘any publicity is good publicity’. No matter why someone retweets, they are endorsing the message and its poster as worthy of being disseminated on Twitter.

With this in mind, it is also worth pointing out here that at least for users who have been on Twitter for some time, and who have ‘learned’ the norms of

194 Discursive Networks of Articulation practice on the platform, a retweet is often perceived as a form of direct and positive endorsement; and it is tied to users’ topics of interest, and their agreement with the content of the tweet (Macskassy & Michelson, 2011).

Consequently, no matter what the reasons or motives behind the retweets, if a group of users retweet in the same way, this effectively leads to the formation of a cluster of users based on similar retweeting (i.e. endorsement) behaviours.

In the following sections, I investigate these clusters in order to explore the dynamics of such formations.

7.2.1 #RoboDebt

The network of retweets in the #RoboDebt case study takes the form of a star- shaped, core–periphery network (M. A. Smith et al., 2014), in which a number of accounts are highly retweeted by most others in the network. These central accounts, which (as discussed in Chapter 6) play the role of opinion leaders and curators of the discussions about the Centrelink controversy, play a key role in the formation of a network of information flows and endorsements. This is especially important when considering the large number of users discussing the Centrelink controversy.

In total, 25,400 distinct accounts contribute to the discussions in the dataset.

Of these, 20,087 are present in the retweet network, with 207,121 edges

(retweets) among them. There are 16,441 distinct Sources (retweeters) and

7,518 distinct Targets (retweetees) in the network; this means that 3,872 accounts have only contributed through retweeting other accounts, and have not themselves received a retweet. The formation of this quite large core– periphery retweet network around the central accounts (Figure 7-1)14,

14 Network created using the following settings in Gephi (Bastian, Heymann, & Jacomy, 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.25, Approximate repulsion enabled; Community

Discursive Networks of Articulation 195 therefore, points to the success of these accounts in forming a community of users circulating their messages, endorsing their views, and amplifying their discourses. As is evident from Figure 7-1, accounts at the periphery of the network are generally those that have contributed to the network through retweeting the central accounts, but have not received retweets themselves.

The visualisation logics of Gephi use curved edges to show the direction of the edge which, in this case, is from the periphery to the core.

Unlike the other two case studies, the retweet network in the #RoboDebt case study does not show any polarisation tendencies. On the contrary, the network of retweets and endorsements transcends the differences and antagonisms between the actors and communities in the Australian Twittersphere: It forms a networked collective of users, gathered to voice their opinion in protest at the flaws in the algorithmic implementation of the debt notice letters.

Figure 7-1: Retweet network of #RoboDebt

detection algorithm: Modularity maximization, as implemented in Blondel, Guillaume, Lambiotte, & Lefebvre, (2008), with a modularity resolution of 1.0 (Lambiotte, Delvenne, & Barahona, 2009)

196 Discursive Networks of Articulation This is particularly important given that the two communities actively tweeting about the issue (Progressive Politics and Hard-Right) show distinct polarisation tendencies in the other case studies. When cross-referencing the accounts in this retweet network with the map of the Australian Twittersphere, it is evident that almost half of the accounts in each of the two antagonistic communities in the Australian Twittersphere have actively tweeted about the

#RoboDebt controversy. Their retweet network, however, does not show any polarisation tendencies. For the Progressive Politics cluster, 2,342 of the 4,024 accounts participated in the #RoboDebt discussion; for the Hard-Right cluster, 409 of the 1,443 accounts participated. Despite their inherent discursive differences, these antagonistic communities actively retweeted each other and the central accounts in the network, thus forming the star-shaped network of retweets.

From this star-shaped network, it can be assumed that the less-connected followers of the accounts in the map of the Australian Twittersphere also participate in similar retweeting patterns, where there is active retweeting between the accounts from antagonistic discourses. However, it is also possible that the star-shaped structure of the network could be due to the fact that all users in the network have retweeted the central accounts, but not each other; that is, it could be an artefact of the limitations raised by a reliance on only the best-connected accounts in the map of the Australian Twittersphere (see

Section 3.8). To account for this possibility, I removed the top 50 core accounts with a weighted indegree of over 1000 from the network, to examine whether the structure of the network changed (Figure 7-2). However, this did not have any significant effect on the overall network structure, and even when the core accounts are absent from the network, no distinct polarisation occurs. Rather, the high level of inter-retweeting between all users, even from the antagonistic

Discursive Networks of Articulation 197 clusters, leads to the formation of a tight-knit, non-polarised network of retweets.

Figure 7-2: Retweet network of #RoboDebt with core accounts removed Two inter-related key findings in thisregard are important. First, the dynamics of the retweet network in this case study, show that Australian Twittersphere users are not unaware of the presence of the antagonistic Other. On the contrary, the fact that the majority of users in the network have retweeted a limited number of central accounts—and each other—shows that a chain of equivalence (Section 2.1.4) is created around the nodal point of social justice.

Second, within the context of this debate, Twitter users have formed a horizontal agonism against a vertical antagonist. In such a space, the inimical antagonisms are replaced by an agonistic space that is formed through the affordance of retweeting. These two pointsbecome clearer once the network of retweets in this case is compared to that of the two other case studies, and the findings are triangulated with those ofthe qualitative examination of the tweets in the network. Therefore, I return to this discussion in Chapters 8 and 9.

198 Discursive Networks of Articulation 7.2.2 Section 18C

Unlike the core–periphery structure of the retweet network in the #RoboDebt case study, the network of retweets in the discussions of 18C exhibits distinct polarisation tendencies, within which a number of key communities show higher levels of intra-cluster than inter-cluster retweeting. Overall, 21,728 distinct accounts are present in the network, with 115,722 edges (retweets) between them. In Chapter 6, I discussed how the highly active users in this case are not necessarily curators of conversations. Rather, they serve as discourse-specific opinion leaders, strategically retweeted only by users from specific discourse communities, rather thanby all, or the majority of users in the network.

The findings of the previous section showthat this polarisation cannot simply be associated with users’ lack of awareness of the presence of the Other, or with what is often referred to as presence of “filter bubbles” (Pariser, 2011) in the network. The findings of the #RoboDebt case study show that the users are in fact actively engaging with the tweets of the Other, strategically choosing which ones to endorse through a retweet, and which ones not to. The act of retweeting, therefore, becomes a discursive strategy, articulating the discourse that a user is attempting to identify with, and amplify, in order to attain certain goals.

I examined the Twitter profiles of the core accounts (highest weighted indegree) in each of the distinct clusters in the retweet network, and also cross-referenced the list of accounts in this network with the map of the Australian

Twittersphere (Bruns et al., 2017), in order to identify what discourse each cluster represents. As discussed in Section 3.8, the map of the Australian

Twittersphere is based on the globally best-connected accounts, while the

Discursive Networks of Articulation 199 retweet network in this case also includes the less-connected accounts.

Therefore, it was necessary to triangulate the findings of my qualitative examination of the profiles in the retweet network with the clusters in the map of the Australian Twittersphere, in order to come to a more objective interpretation of what discourse each cluster represents. Doing so, it became evident that the four main clusters in the retweet network (Figure 7-3)15 are comprised of 1) ‘General Politics and News’: the cluster of media outlets, journalists, and users retweeting them; 2) ‘Progressive Politics’: the more explicitly left-leaning and progressive journalists, activists, influencers, and users retweeting them; 3) ‘Human Rights and Indigenous rights activists’: organisations and actors actively promoting such rights; and 4) ‘Hard-Right’ politics: right-wing and/or (ultra-) conservative figuresand users retweeting them.

Figure 7-3: Retweet Network of the 18C debate

15 Network created using the following settings in Gephi (Bastian et al., 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.25, Approximate repulsion enabled; Community detection algorithm: Modularity maximization as implemented in Blondel et al. (2008), with a modularity resolution of 1.0 (Lambiotte et al., 2009)

200 Discursive Networks of Articulation The overall structure of the retweets network points to the presence of two main antagonistic discourses in the debate. On the one side, there is the quite isolated and less-connected cluster of Hard-Right accounts; on the other, a collective of different communities is formed, with a high level of retweeting interaction among them. These clusters, formed through endorsements and discourse amplifications, potentially pointto an antagonistic frontier, in which the hegemonic struggle revolves around taking a position with regard to Section

18C. Given that such a position is intrinsically a binary choice between keeping or repealing the Section, the polarised network structure is to be expected to some extent. However, what needs further investigation in this regard is the dynamics (material, discursive, agentic, structural) involved in the formation of the antagonistic frontier, and the alliance between the communities in direct opposition to the Hard-Right cluster. After providing a descriptive account of the structure of the retweet network in the next case study, I return to these dynamics in Section 7.3.

7.2.3 Immigration

The retweet network in the Immigration dataset shows similar polarisation tendencies to the 18C case study, especially between the Hard-Right and the rest of the network. However, a number of key differences are also present. The discourse of immigration in Australia is deeply embedded in the international discourse, particularly with regard to the rise of anti-immigration discourses globally (Section 5.4). At the same time, within the domestic environment of the country, this discussion has recently focused on the treatment of refugees and asylum seekers in the offshore detention camps of Nauru and Manus. Both domestic and international aspects of the debate are also evident in the retweet network.

Discursive Networks of Articulation 201 Among the main clusters formed through retweets (figure 7-4)16, the one with the least number of links to the rest of the network is the group of users formed around far-right and (ultra-) conservative discourses. Within this cluster, other than the Australian right-wing figures, there is also a high number of core accounts generally associated with the international conservative and/or far- right movements and public figures, such as Breitbart, Paul Joseph Watson

(@PrisonPlanet), Fox News, and Donald Trump.

Another large cluster of accounts with a high level of intra-retweeting in the network belongs to what can be roughly categorised as ‘the international progressive politics community’. My qualitative exploration of the Twitter profiles of the most retweeted accounts inthis cluster—including how they self- describe in their profile and their latest tweets—points to the fact that a large number of these accounts consists of those with an explicitly progressive and activist online persona, such as Amy Sisking (activist, feminist), Qasim Rashid,

Esquire (Islamic educator, Women’s Rights Defender, Black Lives Matter), and

Shaun King (anti-racism activist, columnist). Additionally, although accounts from a wide variety of international contexts are present in this cluster, most of the widely retweeted accounts in this community are American public figures and media outlets. At the modularity resolution of 1.0 that I used in the visualisation, these accounts are all shown in beige pink in the network.

However, at lower resolutions, this group of accounts is separated into the more explicitly partisan accounts (top-centre of the visualisation), and the more ordinary Twitter users with progressive views (left of the visualisation).

16 Network created using the following settings in Gephi (Bastian et al., 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.03, Approximate repulsion enabled; Community detection algorithm: Modularity maximization as implemented in Blondel et al. (2008), with a modularity resolution of 1.0 (Lambiotte et al., 2009)

202 Discursive Networks of Articulation

Figure 7-4: Retweet network of the Immigration case study In terms of the crowd-sourced elites (Papacharissi, 2015), I have already discussed (in Chapter 6) how the most retweeted accounts in this dataset are associated with different communitiesof users in the Australian Twittersphere.

In this regard, a number of highly retweeted accounts play a central role in bringing different communities of users together. Similar to the #RoboDebt case study, in which activists and information curators play an important role in forming the horizontally agonistic space, public figures such as Kon

Karapangiotidis (Founder and CEO of Asylum Seeker Resource Centre) play an important role in the retweet network of the Immigration discussions. The role of Karapangiotidis, both as a highly active user tweeting about issues related to refugees in Australia, and as an authority in this regard, means that he is retweeted by a large number of other accounts in the Australian

Twittersphere. Similarly, other activists and public figures—such as RISE (the

Refugees, Survivors, and Ex-detainees organisation), Behrouz Boochani

(Iranian-Kurdish journalist detained on Manus), and Josh Butler (journalist and reporter)—play a central role in bringing Twitter users with different

Discursive Networks of Articulation 203 discourses and perspectives together, and giving visibility to the issue of refugee policies in Australia.

7.3 Intra-, Inter- and Extra- Cluster Dynamics of Retweeting

Given the polarisation tendencies observed in the 18C and Immigration case studies, a question remains regarding intra-, inter-, and extra-cluster dynamics:

Is this polarisation the result of the techno-material affordances, design, and algorithmic logics of the platform, or of the discursive logics of the communication environment?

The findings of Chapter 6, and the overall patterns of retweeting discussed in the previous section, point to the presence of divergent patterns in retweeting, sharing URLs, using hashtags, and crowd-sourcing opinion leaders and influencers in different discourse communities. I also discussed how different discourse communities in the Australian Twittersphere are actively involved in each debate. Therefore, in this section, I investigate the dynamics of information flows within, between,and beyond the involved discourse communities, in order to have a more in-depth understanding of what drives the polarisation observed in these two case studies. Furthermore, this section sheds more light on the formation of horizontally agonistic spaces, which was briefly referred to in the previous sections.

The primary objective in this section, therefore, is to investigate the articulatory practices enabled by the material affordance of retweeting, and the inter-relationship of this material affordance with the discourses circulating in the discussions. This, in turn, helps to theorise how the discursive-material

204 Discursive Networks of Articulation knot (Carpentier, 2017) is manifested in the communicative environment of

Twitter, and the dynamics of discursive struggles in each case study.

In order to examine the intra-, inter- and extra- cluster dynamics of interaction, this section compares the retweets occurring within each discourse community with the retweets between the discourse community and other communities, especially its direct antagonists. Returning to the theoretical framework of the study (as discussed in Chapter 2), it is worth emphasising here that by antagonist, I do not only mean an inimical antagonism that is reducible to a binary opposition between two discourses; rather, the concepts of ‘inimical antagonism’ and ‘adversarial antagonism’ (as introduced in Chapter 2) are the primary sensitising concepts explored in this section. This distinction enables comparisons between intra-, inter-, and extra-cluster dynamics, where interactions within each cluster are compared with its inimical antagonist

(inter-cluster dynamics) and its adversarial antagonists (extra-cluster dynamics).

To make the required comparisons, I used the Krackhardt’s E–I index

(Krackhardt & Stern, 1988). This metric (Equation 7-1), developed to study homophily in networks, compares the number of internal links (shown as ‘IL’ in the formula) within a cluster, with the number of external links (shown as

‘EL’ in the formula) between the cluster and all the other nodes in the network.

It is also possible to compare the number of links within a cluster with the number of links between the cluster and another one (rather than all the other nodes in the network). In effect, this metric provides the researcher with a basis of comparison to study intra-, inter-, and extra-cluster dynamics, in that it provides information about whether a certain community engages with another, or simply forms a “filter bubble” (Pariser, 2011) in which only

Discursive Networks of Articulation 205 discursively resonant voices are amplified, with no regard for any other discourses.

ܧܮ െ ܫܮ = ݔ݁݀݊ܫ ܫ –ܧ ܧܮ + ܫܮ

Equation 7-1: Krackhardts E –I Index, where EL is the number of external links and IL is the number of internal links The formula for the calculation of Krackhardt’s E–I index is presented in

Equation 7-1. The outcome index in this formula is always a normalised number between –1 and +1, where a score of –1 means that the cluster is completely isolated (i.e. with no links between it and the rest of the network), and a +1 means that all links between the cluster and the rest of the network are external (which is practically an impossibility, since if all links are external, a cluster loses its meaning). An index of 0 means there is an equal number of internal and external links. In terms of Twitter networks, therefore, negative numbers mean more internal interactions, and positive ones show more engagement with the other clusters. The closer the number gets to –1 or +1, the less/more the community is inward-/outward-facing.

Given that the retweet network of #RoboDebt does not exhibit any clustering tendencies, and forms a star-shaped, core–periphery network instead (M. A.

Smith et al., 2014), there are technically no distinctions possible between internal and external links in this network. Therefore, I focus on the two other case studies in this section.

Before moving to the presentation of the findings of this analytical phase, it is also noteworthy that in order to be able to make the following comparative analyses, I relied on only those accounts present in the network of the

Australian Twittersphere (Section 3.7); that is, the analyses in Sections 7.3.1,

7.3.2, and 7.3.3 focus only on the globally best-connected accounts in the

206 Discursive Networks of Articulation Australian Twittersphere, and not all the accounts in the retweet networks.

This poses some limitations on the study (see Section 3.8). Although the triangulation of these findings with thefindings discussed in Chapters 6 and 8 addresses these limitations to some extent, this reliance on the best-connected accounts is still a limitation of the study.

7.3.1 Section 18C

As discussed in Section 7.2.2, my qualitative examination of the accounts in the clusters formed in the retweet network of 18C, and their cross-referencing with the identified clusters in the map of the Australian Twittersphere, shows that the main active discourse communities in the 18C case study are the

Progressive and Hard-Right clusters. Therefore, in this section, I focus on the dynamics of interaction within and between these communities in terms of retweets. In Section 3.8, I also pointed to some of the limitations of only focusing on the globally best-connected accounts in the Australian

Twittersphere. To address some of these limitations, in the following sections,

I calculate the intra- and inter- cluster dynamics of interaction based on the whole retweet networks; these calculations include accounts both in, and beyond the best-connected accounts in the Australian Twittersphere.

There are 7,279 external retweet links between the Hard-Right cluster and the rest of the network, and 3,303 internal retweets within the Hard-Right community. This gives the Hard-Right cluster an E–I index of 0.375. As for the Progressive cluster, there are 37,924 external links and 16,443 internal ones, giving it an E–I index of 0.395. Both communities, therefore, are in active interaction with the rest of the network, in which information flows from, and to the communities in the form of retweets. In other words, both communities interact with other accounts that are discussing the 18C debate, and actively

Discursive Networks of Articulation 207 retweet them. Furthermore, the fact that there are more external retweets in both communities is an indication that through retweeting other accounts in the debate, these communities are in effect ‘pulling information into’ (Bruns,

2017) their communities, and thus making the messages of the others in the debate visible to their own followers.

Table 7-1: Intra- and extra-cluster dynamics of retweeting in the 18C case study

Extra-Cluster Retweets Hard-Right Progressive External links 7,279 37,924 Internal links 3,303 16,443 E–I Index 0.375 0.395 Looking at inter-cluster dynamics of interaction between the Progressive and the Hard-Right communities, however, the patterns of retweeting change drastically. There are 1,190 retweets between the two communities, in which an account from one community has retweeted an account from the other.

Compared to the number of internal links (3,303 for the Hard-Right and 16,443 for the Progressive), this gives the Hard-Right and Progressive communities an E–I index of –0.470 and –0.865, respectively.

Table 7-2: Inter-cluster dynamics of retweeting in the 18C case study

Inter-Cluster Retweets Hard-Right Progressive External links 1,190 1,190 Internal links 3,303 16,443 E–I Index –0.470 –0.865 Compared to the extra-cluster dynamics of retweeting, which show an active engagement with the rest of the network, the inter-cluster indices indicate that the two communities actively avoid retweeting each other. This active avoidance is particularly pronounced in the case of the Progressive Politics community. The index of –0.865 for this cluster shows that although this community is generally engaged with the rest of the network, it actively avoids

208 Discursive Networks of Articulation interactions when it comes to messages posted by the Hard-Right community.

The opposite is also true to some extent where, overall, there is little interaction with the Progressive discourse by the Hard-Right. However, the difference in the two indices shows that the Hard-Right discourse community generally engages more with the Progressive discourse through retweets.

This observation could, in part, be due to the smaller size of the Hard-Right cluster in the Australian Twittersphere; this smaller size necessitates some level of interaction with the Other to maintain visibility. Also, in some cases, the retweeting of the Progressive could be done to draw the attention of one’s followers to a discursively dissonant message, in order to call them to confront it and to collectively engage with the message/user in a negative way. Another possible reason could be the presence of some mainstream journalists in the

Progressive cluster in the Australian Twittersphere. As discussed in Section

3.8, this happens when a large group of accounts (Progressive cluster here) follows another account (e.g. a journalist); when using a community detection algorithm, this effectively positions that account in the given cluster.

Mere calculations used in this section cannot conclusively show the motivating factors for inter-cluster retweeting. However, the presence of interactions between the two antagonistic communities, and the overall active engagement of both communities with the rest of the network, points to the conclusion that the two antagonistic communities are aware of the existence of the Other in the network. In this sense, the negative E–I indices show that the absence of retweets between the two antagonists is not simply due to the technical affordances of the platform; rather, it is a calculated and strategic discursive practice, to not amplify the other’s voice through a retweet. I return to this

Discursive Networks of Articulation 209 argument at the end of this chapter, and then discuss it in more detail in

Chapter 9.

7.3.2 Immigration

The examination of the Twitter profiles of active users in the Immigration dataset in Chapter 6, and the cross-referencing of the list of accounts with the map of the Australian Twittersphere, points to the presence of a number of active discourse communities in this debate. In Section 6.5.3, I explored the dynamics of activity in the two most active political clusters in this dataset

(the Progressive and the Hard-Right communities). In this section, however, I now delve more deeply into the intra-, inter-, and extra-cluster dynamics of retweeting among all the active clusters in the network.

To identify the most engaged communities in this debate, I compared the number of accounts in each discourse community in the Australian

Twittersphere (Section 3.7) with the number of accounts in that cluster that actively tweeted about the immigration debate in Australia. The ten communities in which more than 20% of the accounts tweeted about the immigration debate are presented in Table 7-3.

Table 7-3: Most active discourse communities in the Immigration case study

Cluster No. of Accounts % of Active Users Writers & Literature 4,496 26.27 Charities & Human Rights Activists 4,302 24.99 Progressive Politics 4,024 60.88 Journalists 2,430 50.37 Education & Teaching 2,417 20.36 Politicians & Political Journalists 2,018 30.03 Hard-Right Politics 1,443 42.34 Progressive Political Commentators 1,276 42.87 Lawyers & Legal Scholars 998 35.87 Science & Research 921 35.61

210 Discursive Networks of Articulation Starting from the overall level of engagement of each community with the rest of the network—or the metrics of extra-cluster interactions—I compared the number of internal retweets within each of the active clusters with the number of retweets between the given cluster and the rest of the network. Table 7-4 is a summative account of the E–I indices for extra-cluster dynamics of retweeting.

Table 7-4: Extra-cluster dynamics of retweeting in the Immigration case study

Cluster Retweet E–I Index Science & Research 0.965 Education & Teaching 0.920 Progressive Political Commentators 0.877 Writers & Literature 0.836 Charities & Human Rights Activists 0.801 Politicians & Political Journalists 0.799 Lawyers & Legal Scholars 0.780 Journalists 0.694 Hard-Right Politics 0.672 Progressive Politics 0.137 As can be seen from Table 7-4, the E–I indices for almost all involved communities in the Immigration dataset is close to +1 (with the exception of the Progressive Politics cluster); this indicates a very high level of engagement with the rest of the network in terms of retweets. In this sense, it is evident that each cluster of users is actively interacting with the other discourses in the discursive environment. They do this by retweeting their messages to introduce them to their own followers and to amplify their discursive position, or by ‘pulling information into’ their cluster, as formulated by Bruns (2017).

The discourse community with the least level of extra-cluster retweet interaction with the rest of the network is the Progressive Politics cluster.

However, this community is still highly engaged with the rest of the network, in that there are more external retweets between this cluster and the rest of the network than there are internal retweets.

Discursive Networks of Articulation 211 As the discussion in Section 7.2.3 showed, the discourse communities in the

Immigration dataset actively interact with the international accounts and actors. Therefore, it is possible that the highly positive E–I indices in Table 7-

4 could partly be due to the interactions with the global discourses. While, on its own, it shows a high level of embeddedness in the global discourse of immigration, and a high level of visibility for each of the discourse communities involved in the debate, it is necessary to also investigate the interactions between these communities within the Australian context. This investigation will determine whether the overall network of interactions forms large clusters of users who only amplify their own voice (i.e. filter bubbles and/or echo chambers), or if there are interactions and discursive alliances among communities.

Table 7-5 presents the inter-cluster dynamics of retweeting between the active clusters in the immigration debate. Here, a number of patterns emerge. First, regarding the Progressive community, which is among the most highly engaged communities tweeting about the discourse of immigration in Australia, the inter-cluster E–I indices show that although this community is highly retweeted by the other clusters in the network (with the exception of the Hard-Right), it does not necessarily engage with them. In other words, the retweeting relationship between this community and the others is not a reciprocal one. In light of the finding that this community also received the lowest E–I index for its extra-cluster dynamics of retweeting, this could potentially mark the presence of filter bubble tendencies in this cluster; that is, the Progressive community actively amplifies its owndiscourses internally, but does not introduce nuanced discourses into its community.

212 Discursive Networks of Articulation Of course, the high level of retweets received by the Progressive cluster could also be in part due to the sheer size of the cluster, and the generally high level of activity of accounts in this community (Bruns et al., 2017) that make it more visible in the Australian Twittersphere. This factor effectively makes tweets originating from this cluster more visible to the broader discursive environment, hence increasing their likelihood of being retweeted. Also, the fact that this community is highly retweeted by almost all the other clusters in the network also points to a sense of endorsement of its discourses by the other communities involved in the debate. In this sense, the Progressive community becomes an opinion leader for the other communities in the network that actively and strategically align themselves with the discourse of the Progressive cluster, and form discursive alliances through the act of retweeting.

Table 7-5: Inter-cluster dynamics of retweeting in the Immigration case study (Numbers above 0.0 shown in bold; Prog. Com. = Progressive Political Commentators)

Retweeting Cluster

Prog. Com. Hard-Right Progressive Progressive Journalists Journalists Politicians Education Literature Literature Activists Lawyers Lawyers Science Science

Activists -0.973 -0.724 -0.189 -0.466 -0.940 0.194 -0.319 -0.596 -0.530

Hard-Right -0.819 -0.987 -0.952 -0.641 -0.992 -0.742 -0.944 -0.970 -0.912

Literature -0.645 -0.998 -0.150 -0.657 -0.923 -0.029 -0.393 -0.118 -0.583

Lawyers -0.363 -0.995 -0.453 -0.565 -0.873 0.027 -0.226 -0.467 -0.615

Politicians -0.423 -0.935 -0.708 -0.378 -0.833 0.009 0.030 -0.239 -0.326

Retweeted Cluster Progressive 0.625 -0.849 0.617 0.867 0.841 0.879 0.801 0.782 0.661

Science -0.834 -0.998 -0.918 -0.828 -0.894 -0.987 -0.786 -0.717 -0.902

Education -0.782 -0.998 -0.858 -0.643 -0.632 -0.970 -0.367 -0.846 -0.849

Prog. Com. -0.712 -0.997 -0.439 -0.479 -0.463 -0.926 0.285 -0.620 -0.413

Journalists -0.118 -0.968 -0.331 -0.057 0.079 -0.837 0.368 -0.061 0.232

Another community with progressive views is the cluster of accounts labelled

‘Progressive Politics Commentators’ in the map of the Australian

Twittersphere. A qualitative examination of the Twitter profiles of this group showed that users in this cluster often use explicitly partisan self-descriptions

Discursive Networks of Articulation 213 in their profiles. What differentiates this group of users from other users with progressive views, is their explicit marking of their discursive positions.

Therefore, what marks their progressive views is not only the content of their tweets, but also their online personas. In other words, this community could be classified either as ‘explicitly progressive’ or as ‘hard-left’.

Regarding the inter-cluster dynamics of retweeting for this community, Tables

7-4 and 7-5 show that although accounts in this cluster actively retweet a variety of other communities, they are not generally retweeted back. This pattern stands in direct contrast to the patterns observed for the Progressive community. Given that this cluster has a very high level of retweet interaction with the rest of the network, it appears that its highly partisan views are not endorsed by the other communities engaged in the immigration debate, in a similar fashion to the tweets of the Hard-Right community.

Another highly partisan community of users in this regard is the cluster of

Hard-Right users. Although—as Table 7-4 shows—this community is actively retweeting a large number of accounts in the overall network, its inter-cluster dynamics of retweeting make it a quite isolated discourse community. Within the context of the Australian Twittersphere, and the active communities tweeting about the immigration debate, this community has neither retweeted nor been retweeted by any other clusters to any significant degree. Therefore, its highly positive E–I index (in terms of extra-cluster dynamics) potentially points to its engagement with discursively resonant voices from outside the

Australian Twittersphere (e.g. international far-right discourses). This finding is supported by the findings discussed in Chapter 6 (Section 6.3.3), which show the strong presence of the international far-right discourses in the tweets posted by this community.

214 Discursive Networks of Articulation 7.3.3 Summative Remarks

Taking the differences between intra-, inter-, and extra- cluster interactions into account, it becomes clearer how the act of retweeting is a calculated strategy to amplify discursively resonant voices, and to hamper dissonant ones.

On comparing the 18C and Immigration case studies with the #RoboDebt debate, and the dynamics of interaction within and between clusters of users, it is evident that the Twitter users involved in each debate are aware of the presence of the Other. However, the choice between seeing the antagonist as an enemy or adversary presents itself when a user is faced with an issue. Once a controversy arises, it is the discourses and symbolic resources of the user that play a formative role in the user’s decision to identify themselves with another discourse and form chains of equivalence; or, on the contrary, to intensify their differences. In this regard, the symbolicmeanings ascribed to the affordances of Twitter as “semiotic technologies” (Zappavigna, 2018) are constantly negotiated and contingent. At times, retweets can be used to intensify antagonisms and, at other times, they serve to form agonistic networks of discursive alliances.

In strategically employing the affordance of a retweet, therefore, users negotiate the identity of themselves and the Other, whether they engage with the tweet or not. When actively retweeting another user, the articulatory relation created between the two users positions them vis-à-vis each other, through intensifying their discursive position and maintaining the endorsement relationship between them. In this sense, the users are articulated as discursive allies. On the other hand, the act of not retweeting, or remaining impassive and silent in relation to the tweets of the Other, is also an articulatory practice formed through absence, silence, and difference. As shownin this chapter, the practice of ‘no

Discursive Networks of Articulation 215 platforming’ can be achieved through the absence of engagement with an affordance such as a retweet. Upon seeing a tweet, and not endorsing it through dissemination, a user amplifies the logics of difference between them and the discourse of the user posting a tweet. In this sense, who/what a user retweets or not, always forms an articulatory relation between the users, their discourses, and the material affordance of retweeting, within which the identity of each is contingent on its relation to the Other.

Briefly put, this section showed how the act of retweeting per se is a discursive strategy and an articulatory practice, manifested through the material affordances of Twitter as the platform on which communications are taking place. At the same time, the very act of retweeting, and the decision to retweet, are always embedded in the discourses of the articulating users, who are constantly faced with choices to retweet the Other and enter a chain of equivalence, or not to retweet and remain actively passive and form a chain of difference. Collectively, these choices, strategies, and articulatory practices form clusters of networked discursive alliances, and potentially lead to an agonistic space in which an issue can be discussed. On the other hand, strategic collective avoidance and intensification of the antagonisms create chains of differences and frontiers of inimicalantagonisms; thus, the possibility of agonistics is practically voided.

7.4 @Mention Networks

The communicative and discursive functions of @mentions are fundamentally different from those of retweets. While retweets perform functions such as information dissemination, endorsement, amplification of voice and the like,

@mentions are mostly employed to perform interpersonal communicative and discursive functions, whether positively or negatively. As a micro-level layer of

216 Discursive Networks of Articulation communication (Bruns & Moe, 2013), an @mention of another account could be an attempt to start a conversation, to give visibility to the account, to promote it, or to criticise it. Whatever connotation a user has in mind when

@mentioning another, the very act of @mentioning creates a form of interaction between the two accounts, whether the one-way @mention is followed by a conversation or not (Murthy, 2018, p. 144).

In this light, patterns formed through collective @mentions among the accounts in a dataset effectively indicate clusters of interaction, where collectives of users interact with certain actors and, possibly, with each other. This has two implications for this study: first, such clusters point the research to areas of interest regarding who talks to/at/about whom, which is followed by the question why in the next chapter. Second, the structures of such networks potentially guide this study towards the dynamics of interactions, and whether there are any potentials and possibilities of an agonistic space formed through the collective use of the material affordances of the platform. In the following sections, therefore, I first start with anexploration of the overall structures of networks of @mentions in each case study, and follow this exploration with an investigation of the dynamics of intra-, inter-, and extra- cluster interactions formed through @mentions.

7.4.1 #RoboDebt

As was the case for its retweet network, the @mentions network of the

#RoboDebt case study (Figure 7-5)17 also forms a star-shaped, core–periphery structure (M. A. Smith et al., 2014), in which a number of key accounts are

17 Network created using the following settings in Gephi (Bastian et al., 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.2, Approximate repulsion enabled; Community detection algorithm: Modularity maximization as implemented in Blondel et al. (2008), with a modularity resolution of 1.0 (Lambiotte et al., 2009)

Discursive Networks of Articulation 217 widely @mentioned by the majority of other accounts in the network. The account @mentioned by most users (highest indegree) is that of @Centrelink.

This, of course, is to be expected given that Centrelink was both the topic of discussions and a keyword used in the data collection.

Other key accounts central to the @mentions network can roughly be categorised into three distinct groups. First, activists such as @Asher_Wolf, who is also the most active account in the dataset (Section 6.5.1.1), have received the highest number of @mentions in the network. This indicates the activists’ perceived roles as opinion leaders and as curators of the discussions, in that they are extensively @mentioned by other accounts in the network to bring issues to their attention; to refer to them as authorities in the debate; and in effect, to increase their visibility and discursive power. An exploratory qualitative reading of the tweets @mentioning this group of users showed that in general, this category of users are @mentioned in tweets with content that aims to promote their role, endorse their discourse, extend their arguments, or amplify their messages.

The second category of users widely @mentioned are the Twitter accounts of politicians, especially those who are involved in matters related to welfare, such as Alan Tudge (then Minister of Human Services) and Linda Burney (Shadow

Minister for Human Services at the time). Also in this category is the then prime minister, Malcolm Turnbull, who is @mentioned extensively due to his political position. Tweets addressed to these figures, in general, are either critical of them, or invite them to take action and fix the problems raised by the algorithmic debt notices.

Finally, the third category of the most @mentioned accounts are those of journalists and media outlets, such as The Sydney Morning Herald, ABC News,

218 Discursive Networks of Articulation and Peter Martin (journalist and reporter). Tweets with @mentions of these types of accounts are a mix of endorsements, dissemination of their tweets, and attempts to bring their attention to various issues regarding RoboDebt that are discussed on Twitter.

Figure 7-5: @Mentions network of #RoboDebt Similarly to the retweet network of this case study, removing the core accounts

(top 36 accounts with a weighted indegree of over 2000) from the network does not change the overall structure (Figure 7-6). In other words, the core– periphery structure of the network is not simply because the collectives of accounts in the @mention network exclusively @mention the core accounts.

Rather, in addition to @mentioning the core accounts, there is also a high level of interaction and conversation among the users in the network.

Discursive Networks of Articulation 219

Figure 7-6: @Mentions network of #RoboDebt with cores removed Similar structures in @mentions networks have been observed in other studies.

Conover et al. (2011), for example, found that while the retweet network in their study of political discussions on Twitter showed extreme polarisation tendencies, such was not the case in the network of @mentions. However, they also found that this was the case because one community actively engaged with the antagonist to inject their discourse into the conversations. The major difference in this regard is that the interactions between antagonists in the

#RoboDebt network are not performed to force an ideology into the discourse of the Other through @mentions. Rather, as the qualitative examination of tweets in Chapter 8 shows (Section 8.1.1), there are genuine interactions, endorsements, and conversations taking place. Of course, this is to a large degree due to the nature of the #RoboDebt controversy, which is not directly a political topic.

Therefore, the structure of the @mentions network in this case points to a potentially agonistic discursive space, in which users from the different clusters in the Australian Twittersphere collectively talk to/at/about similar issues, refer to the same opinion leaders, and disseminate similar information. At the

220 Discursive Networks of Articulation same time, the central position of politicians in this network also points to a degree of vertical antagonism. In other words, the agonistic space created here is formed around a common goal, or a consensus over a nodal point in the discourse, where the Other-enemy is no longer another discourse community in the horizontal context of the Australian Twittersphere. Rather, the common antagonist for all users in the network is positioned in the vertical context of the debate. This creates a horizontal agonism and chain of equivalence on

Twitter, in oppositional relation to a vertical antagonism. While the discourse communities involved in the debate are aware of the differences between them and, as shown in the two other case studies, are in an inimical antagonistic relation with regard to other issues, a chain of equivalence has been created in this debate: The vertical antagonism has connected them all in a horizontally agonistic space.

7.4.2 Section 18C

The extreme polarisation that was observed in the retweet network of the 18C case study is not as pronounced in the network of @mentions in this dataset

(Figure 7-7)18. However, there is still some level of clustering tendencies evident from the @mentions network; this is due to the different communicative functions achieved through the act of @mentioning another account.

The account receiving the highest number of @mentions in the network, both in terms of the number of users @mentioning it (indegree) and the total number of @mentions received (weighted indegree), is the Twitter account of the then prime minister Malcolm Turnbull. This, of course, is to be expected given his

18 Network created using the following settings in Gephi (Bastian et al., 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.3, Approximate repulsion enabled; Community detection algorithm: Modularity maximization as implemented in Blondel et al. (2008), with a modularity resolution of 1.0 (Lambiotte et al., 2009)

Discursive Networks of Articulation 221 political position. Apart from this account, the three biggest clusters of accounts formed through collective @mentions are groups of accounts which could be roughly categorised as (ultra-) conservative public figures; progressive and left-leaning public figures; and Indigenous rights activists and organisations.

Figure 7-7: @Mentions network of 18C With regard to the (ultra-) conservative, right-wing, and far-right accounts, those receiving the highest number of @mentions are right-wing politicians such as Cory Bernardi, Pauline Hanson, and Tony Abbott; conservative journalists such as Rita Panahi and Andrew Bolt; and conservative media outlets such as The Australian. On the other hand, another cluster in the

@mentions network consists of progressive, left-leaning, and Australian Labor

Party (ALP) accounts, including journalists such as Alice Workman; politicians such as Sam Dastyari (ALP senator) and Mark Dreyfus (ALP member of parliament); and media outlets such as The Financial Review and

The Sydney Morning Herald. A smaller cluster of accounts in this network is comprised of activists and organisations that promote and campaign for

222 Discursive Networks of Articulation Indigenous rights in Australia; for example, the National Aboriginal

Community Controlled Health Organisation (NACCHO Australia) and

Indigenous X.

Overall, the observed clustering tendencies in the network of @mentions points to the fact that some groups of accounts are more @mentioned by certain other groups of accounts. However, this exploratory investigation of the network could not alone provide an answer to who @mentions whom. For instance, it was not clear at this stage whether a conservative politician is mostly

@mentioned by conservative users (e.g. to invite them to address the issue, endorse their message, or give them visibility) or progressive users (e.g. to criticise them). Answering such questions required further exploration of the network and its dynamics. Before moving to the more in-depth investigation of the dynamics of @mentioning in this case study, I first also provide the descriptive and exploratory findings of the Immigration case. This is necessary to gain a better comparative basis between the polarised debate over 18C and the multipolar discussions of immigration.

7.4.3 Immigration

The @mentions network for the immigration case study (Figure 7-8)19 takes a similar form to that of the 18C, in that it is not extremely polarised, yet has distinct clustering tendencies. Of course, given the multipolar nature of the immigration debate, and the fact that it is simultaneously related to a number

19 Network created using the following settings in Gephi (Bastian et al., 2009): Gravity: 1.0, LinLog mode enabled; Scaling: 0.03, Approximate repulsion enabled; Community detection algorithm: Modularity maximization as implemented in Blondel et al. (2008), with a modularity resolution of 1.0 (Lambiotte et al., 2009)

Discursive Networks of Articulation 223 of different discourses and debates, the clustering tendencies in the network are to be expected to some extent.

A qualitative review of the Twitter profiles of the core accounts in the different clusters in the network of @mentions helped to roughly categorise the main clusters into five main groups. The largest group of @mentioned accounts are the Twitter accounts of Australian politicians who are directly related to the immigration debate within Australia, especially with regard to the offshore detention camps. Politicians such as the then prime minister Malcolm

Turnbull, his successor Scott Morrison (who launched the Operation Sovereign

Borders in Australia in 2013), and the Immigration Minister Peter Dutton

(who is known for his strict stance against illegal immigration) are the key accounts in this cluster20.

Also highly connected to this cluster are the Twitter accounts of mainstream and well-established media outlets in Australia, such as ABC News, Sydney

Morning Herald, and The Age; and political television programs such as Q&A and Four Corners.

Another cluster of highly @mentioned accounts consists of (ultra-) conservative and right-wing politicians, public figures, and media outlets in the Australian socio-political contexts; for example, Pauline Hanson; Chris Kenny

(conservative political commentator); The Australian; and Rita Panahi.

20 During the time period in which the data for this case study was collected, there was a spill in the Coalition, in which Peter Dutton challenged Malcolm Turnbull to become the party leader and the prime minister. Eventually, neither won the party vote, and Scott Morrison became the prime minister. Given that Dutton was the Immigration Minister, and one of the keywords I used to collect data was ‘immigration’, it is likely that some of the tweets in the dataset refer to news and tweets about the event. While I did my best to remove such tweets, some might remain.

224 Discursive Networks of Articulation International and, especially, American right-wing and conservative accounts also form a large cluster in the @mentions network, with accounts such as

Donald Trump and Fox News playing a central role in this cluster. However, also in this cluster are the Twitter accounts of other non-conservative American media outlets such as CNN and Washington Post. This, of course, could be again due to the different communicative functions of @mentioning on Twitter, which can be used to both endorse and criticise others.

Finally, a smaller cluster of accounts belongs to the activist organisations primarily set up to promote, support, and campaign for refugee rights around the world, such as UN Refugees and also (in Australia) RISE. These accounts are highly connected to the cluster of Australian politicians, media outlets, and journalists.

Figure 7-8: @Mentions network of Immigration case study However, as pointed out in the previous section, although the exploration of the @mentions network provided useful insights into the structure of the network overall, it could not show intra- and inter- cluster dynamics on its

Discursive Networks of Articulation 225 own; hence, a more in-depth investigation of who @mentions whom, and why, was required. In the next section, therefore, I delve more deeply into such dynamics with regard to both the 18C and the Immigration case studies.

7.5 Intra-, Inter-, and Extra- Cluster Dynamics of @Mentions

Following the findings related to the clustering tendencies in the networks of the 18C and Immigration case studies (as discussed in the previous section), and the findings related to the dynamics of retweeting within, between, and beyond antagonistic clusters (as discussed in Section 7.3), I now move to the investigation of such dynamics with regard to the articulatory practice of

@mentioning.

7.5.1 Section 18C

The two main communities in the Australian Twittersphere that have actively contributed to the discussions in the 18C dataset—Progressive and Hard-Right politics—are both outward-looking in terms of @mentioning others, in that they both actively engage with other accounts from outside their communities through @mentions. Of course, as discussed in Section 4.3.1, @mentions could be employed to achieve different communication goals. The Progressive and

Hard-Right clusters both received positive E–I indices, indicating that both communities ‘push information’ outside their own clusters and followers

(Bruns, 2017), by actively @mentioning accounts in other discourse communities. In this sense, they both actively attempt to gain visibility and discursive power through interactions with accounts other than those of their own followers.

226 Discursive Networks of Articulation Table 7-6: Extra-cluster dynamics of @mentions in the 18C case

Extra-Cluster @Mentions Hard-Right Progressive External links 7,283 18,308 Internal links 2,467 6,556 E–I Index 0.493 0.472 However, when considering the inter-cluster dynamics of @mentioning between the two antagonistic communities, both show negative E–I indices. This points to more internal conversations within the communities than inter-cluster debates. This active avoidance of engaging with the Other is more pronounced in the Progressive discourse community, which received an E–I index of –0.608 compared to the –0.214 for the Hard-Right community. In other words, in line with the findings of Conover et al. (2011), the Hard-Right community attempts more to engage with the Progressive; however, this interaction is not reciprocal.

Of course, it is worth emphasising again here that these indices do not necessarily and conclusively provide explanations of why this happens. The engagement of the Hard-Right with the Progressive could be due to their attempt to criticise the antagonist; to inject their discourse into the discussion; to bring the attention of their followers to a particular user in the antagonistic community, to then engage in a collective Twitter attack on the account. Also, this engagement could—at least partly—be due to the smaller size of the Hard-

Right cluster in the Australian Twittersphere, which inevitably forces this cluster to engage more with the broader discursive environment in order to attain and maintain visibility and discursive power.

Table 7-7: Inter-cluster dynamics of @mentions in the 18C case

Inter-Cluster @Mentions Hard-Right Progressive External links 1,597 1,597 Internal links 2,467 6,556 E–I Index –0.214 –0.608

Discursive Networks of Articulation 227 No matter the reasons for the scarcity of inter-cluster interactions, however, one thing that is clear enough from the E–I indices: Both antagonistic communities are, in fact, aware of the presence of the Other in the Australian

Twittersphere. In other words, the two antagonists are not simply active in a

‘filter bubble’ where they are hermetically sealed from each other (Bruns,

2017), and are not exposed to the antagonist discourse. On the contrary, they are indeed aware of the presence of the antagonist, but strategically choose not to engage with them. This could be seen as a discursive strategy of ‘no platforming’ that deprives the antagonist of the possibility of gaining visibility and discursive power. In this sense, the active passivity towards the Other is, in itself, an important part of the antagonism and hegemonic process. Through not engaging with the discourse of the Other-enemy, one is effectively silencing them, depriving them of a platform to engage in a lengthier interaction than was provided by the original tweet. This non-engagement, therefore, becomes part of the discursive struggle. As Slavoj Žižek rather polemically puts it,

“sometimes, doing nothing is the most violent thing to do” (Žižek, 2008b, p.

183).

7.5.2 Immigration

The dynamics of inter-cluster @mentioning in the Immigration dataset show similar patterns of interaction to both its retweet dynamics, and to the

@mentioning patterns in the case of the 18C study.

Starting from the broad patterns of @mentioning and examining the extra- cluster dynamics of interactions, it is evident that all discourse communities in the Australian Twittersphere that are involved in the debate are actively

‘pushing’ information outside their clusters (Bruns, 2017) through actively engaging with the rest of the network. Overall, almost all active clusters in the

228 Discursive Networks of Articulation Australian Twittersphere that have tweeted about the immigration debate received E–I indices above zero, thus indicating their active engagement with the broader discursive environment. The Progressive Politics cluster shows the lowest level of extra-cluster interactions compared to the rest of the communities; this was also the case for its retweeting dynamics. However, compared to its retweets, which still remain more outward-looking, this community shows slightly fewer extra-cluster @mentions than intra-cluster interactions.

Table 7-8: Extra-cluster dynamics of @mentions in the Immigration case study

Description @Mentions E–I Index Science & Research 0.872 Education & Teaching 0.816 Progressive Political Commentators 0.790 Charities & Human Rights Activists 0.693 Writers & Literature 0.662 Journalists 0.657 Lawyers & Legal Scholars 0.626 Hard-Right Politics 0.568 Politicians & Political Journalists 0.510 Progressive Politics –0.056 However, when investigating the inter-cluster dynamics of @mentioning, a few interesting patterns started to emerge. First, with regard to interactions between the Progressive discourse community and all the other clusters in the network, it is evident that all the involved communities actively engage with the Progressive discourse. This is even the case for the Hard-Right cluster, as the Progressive community’s inimical antagonistic discourse. What this potentially shows is that the different discourses in the debate interact with the Progressive community—as the opinion leader of the discussions around refugees in Australia—by actively @mentioning accounts in this cluster; however, this interaction is not reciprocal. An investigation of the interactions of the Progressive cluster with the other communities in the dataset showed

Discursive Networks of Articulation 229 that there is not much @mentioning of other communities by the Progressive community.

For the Hard-Right discourse community, the dynamics of @mentioning are quite different. The only cluster receiving a high number of @mentions by the

Hard-Right is its inimical antagonist, the Progressive community. This potentially indicates the appropriation of the @mentioning affordance of

Twitter to perform a horizontal antagonism on the platform, through active confrontation with the discourse of the Other-enemy. On the other hand, the

Progressive cluster does not reciprocate this confrontation, effectively not participating in the discursive clash. This, as discussed in the previous section, could be understood as an active passivity towards the antagonist, in order to deny them the platform to amplify their visibility and discursive power.

The only community which actively @mentions the Hard-Right is that of the

‘Politicians and Political Journalists’, which also actively engages with the discourse of the Progressive—and at a much higher level. This, of course, is to be expected to some extent, since the logics of the discourse of journalism require a balanced approach by the journalists, who are expected to provide views from both sides of the debate. Additionally, given that a number of the core accounts in the Hard-Right cluster are (ultra-) conservative politicians, a political journalist inevitably has to refer to them when writing a story or tweet about the immigration debate.

230 Discursive Networks of Articulation Table 7-9: Inter-cluster dynamics of @mentions in the Immigration case study (Numbers above 0.0 shown in bold; Prog. Com. = Progressive Political Commentators)

@Mentioning Cluster

Prog. Com. Hard-Right Progressive Progressive Journalists Journalists Politicians Education Literature Literature Activists Lawyers Lawyers Science Science

Activists -0.908 -0.760 -0.494 -0.380 -0.931 -0.227 -0.573 -0.664 -0.446 Hard-Right -0.621 -0.690 -0.118 0.211 -0.760 -0.390 -0.418 -0.238 -0.384 Literature -0.681 -0.900 -0.692 -0.430 -0.952 -0.505 -0.688 -0.515 -0.557 Lawyers -0.623 -0.800 -0.836 -0.517 -0.921 -0.780 -0.757 -0.668 -0.604 Politicians -0.151 -0.317 -0.359 -0.134 -0.451 0.146 0.145 0.102 -0.060 Progressive 0.512 0.414 0.213 0.678 0.904 0.611 0.592 0.687 0.751 @Mentioned Cluster Science -0.866 -0.980 -0.948 -0.960 -0.830 -0.989 -0.902 -0.874 -0.893 Education -0.847 -0.950 -0.922 -0.884 -0.604 -0.973 -0.759 -0.908 -0.821 Prog. Com. -0.821 -0.884 -0.799 -0.753 -0.468 -0.941 -0.553 -0.857 -0.576 Journalists -0.300 -0.772 -0.553 -0.327 -0.137 -0.795 -0.177 -0.373 -0.124

The complexities involved in the conceptualisation of @mentioning on Twitter did not allow this stage of the study to provide explanations about why certain communities @mention others. However, the exploration of who @mentions whom does show patterns; this could effectively lead to the formation of more questions (i.e. sensitising concepts) to further explore in the qualitative reading of each community’s tweets. As a case in point, an examination of the dynamics of inter-cluster @mentioning showed that the Hard-Right discourse community actively engages with its antagonist through @mentions. However, at this stage, it is not clear why they do so; nor is it clear whether this dynamic should be interpreted as a form of endorsement (which is highly unlikely), an attempt to engage in conversations, an act of criticism, or an invitation to attack by one’s followers. The next chapter deals with these questions.

7.5.3 Summative Remarks

This section showed how different communities involved in the debates over

Section 18C of the Racial Discrimination Act in Australia and issues related to the immigration discussions strategically use the @mentioning affordance of

Discursive Networks of Articulation 231 Twitter to both sediment and amplify antagonisms, and to form networks of discursive alliances through collectively @mentioning the hegemonic and opinion-leading discourse communities.

Regarding antagonism, the exploration of the dynamics of @mentioning within, between, and among clusters showed that although users actively engage with various communities in order to disseminate their discourse, they strategically avoid interacting with their inimical antagonist, in order to deny them the same possibility of visibility through @mentions. This active passivity towards the Other-enemy therefore becomes, per se, a signifier of difference and antagonism. This also points to the different appropriations of the affordance of @mentioning, through which the act of @mentioning the Other sometimes becomes an agonistic articulatory practice; that is, it forms networked discursive alliances, and can sometimes act in the opposite way, in bringing differences and antagonisms to the forefront.

The analysis in this section also points to the way that the formation of networked discursive alliances is at the same time also influenced by the overall logics of the platform. In forming strategic networks of discursive alliances, the

Progressive cluster in the network becomes the leading community, with others forming alliances with the Progressive community. This is directly influenced by two of the core logics of Twitter as a platform.

First, the expectation of activity, and its correlation with visibility on the platform, plays an important role in this regard, in that the users in the

Progressive community are often among the most active users in the discussions of offshore detention camps in Australia. This potentially makes them more visible to other users in the Australian Twittersphere, and more likely to be interacted with.

232 Discursive Networks of Articulation Second, the sheer size of the Progressive cluster in the network of the

Australian Twittersphere, and the strong intra-cluster dynamics of retweeting and @mentioning within this community, also potentially means that messages originating from this cluster have a higher chance of being picked up by both the algorithms of Twitter and the other users in the network. This is because such messages are more likely to get a boost from intra-cluster retweets and

@mentions, and this makes them more likely to be shown to other users, either in the trending topics section or in the curation of Twitter feeds. Under these logics, then, it is often the smaller discourse communities who form the alliance with the Progressive cluster, making it a cluster of crowd-sourced elites

(Papacharissi, 2015) who lead the discussions. Concurrently, the sheer size of the cluster, its hegemonic position in the Australian Twittersphere, and its strong homophily and intra-cluster interactions make this discourse more sedimented, and its users less likely to feel the need to interact with others in order to become visible and/or hegemonic.

Finally, the demographics of Twitter users could also play a role in this regard.

It has been shown that Twitter, overall, has more left-leaning and progressive users (Watkins et al., 2017), and this potentially makes it more likely for such discourses to become dominant and hegemonic on the platform. In effect, this very demographic could also be another explanation for why the Hard-Right community has to engage more with the discourse of the antagonistic Other.

The very act of discursive confrontation and struggle requires a high level of energy and commitment. Therefore, under the principle of least effort, a user whose identity and discourse are already highly sedimented and hegemonic does not feel the need to amplify their radical differences with the Other through engagement. On the contrary, a user whose very presence on the

Discursive Networks of Articulation 233 platform is dependent on the amplification of differences is more likely to interact with the antagonist.

7.6 Conclusions

In this chapter, I explored and investigated the different network structures formed in each case study through the collective employment of retweets and

@mentions, as two material affordances of Twitter.

In the first case study (#RoboDebt), the structure of the networks of retweets and @mentions pointed to the presence of an agonistic space transcending the horizontal antagonisms in the Australian Twittersphere. A number of key accounts curating and channelling the conversations, setting the agenda, and framing the debates, played a central role in the formation of this agonistic space. At the same time, this space was also formed through the affordances of retweeting and @mentioning, thus enabling the participants in the debate to endorse each other’s messages, amplify the discourses, increase the visibility of the conversations, and create a cycle of information flows circulating both on and beyond the platform. The term and hashtag #RoboDebt, coined by the activists on Twitter in reference to Centrelink’s automated debt notices, was picked up by journalists and media outlets, who then wrote pieces on the issue, with reference to the information circulating on Twitter. These were again circulated back to the platform by the very activists curating the debate, and were disseminated further by other users involved in the debate.

The observed agonism in the case of #RoboDebt was not, however, completely manifested in the 18C debate. Rather, the network analysis of the discursive environment of this debate pointed to the presence of both agonistic and antagonistic formations. On the agonistic side, a discursive alliance was formed

234 Discursive Networks of Articulation between the communities that were against repealing Section 18C. This networked discursive alliance was formed in an antagonistic frontier in opposition to both the presence of the anti-18C community in the horizontal context of the platform, and at the same time, to the vertical context of the politicians calling for changes to the country’s Racial Discrimination Act.

The investigation of the information flows and interactions within, between, and among the discourses involved in this debate also showed that the observed antagonism between the Hard-Right discourse and the rest of the network is not merely the result of the technological design of the platform. On the contrary, it is the result of choices and discursive strategies that amplify one’s discursive alliance with some, and antagonism against others. These all manifest through the collective use of the affordances of retweeting and

@mentioning on Twitter.

Similar patterns were also observed in the third case study in this project, which explored discourses around the topic of immigration. Unlike the first two studies, the immigration debate is not an exclusively Australian topic; rather, it is deeply embedded in global discourses. This embeddedness showed itself in the networks of retweets and @mentions in this case study. In a similar way to the other case studies, the network formations in this case also showed how networked discursive alliances are formed through the collective use of the affordances of Twitter. At the same time, the analysis in this regard showed how the meanings ascribed to these affordances are contingent on users’ employment of them. @Mentions, for instance, were shown to be both used to amplify endorsements and visibility, and antagonisms and differences.

Finally, a comparison of the three case studies also showed how the formation of networked discursive alliances is also embedded in the logics of equivalence,

Discursive Networks of Articulation 235 difference, and hegemony. Reflecting on the demographics of Twitter users in

Australia, and the number of users in each community involved in the different debates investigated in this chapter, it became clearer how chains of equivalence are formed in each case. In the formation of networked discursive alliances, it was shown how smaller and less hegemonic discourses often use

Twitter’s affordances to ally themselves with the hegemonic discourse of progressive politics on the platform. Through an examination of the inter- cluster dynamics, it became evident that the strong links between the

Progressive Politics cluster and the other discourses involved in the discussions, are non-reciprocal. Rather, it is the less sedimented and hegemonic discourse communities that ally themselves with the hegemonic one. On the other hand, across the antagonistic frontier between the Hard-Right and the Progressive discourses, the community of Hard-Right users resorts to active negative engagement with their Other-enemy to amplify the logics of difference. At the same time, this active engagement is necessary for this community to maintain its visibility in the Australian Twittersphere.

The same is not true for the hegemonic discourse of Progressive Politics, however. Its hegemonic position in the network, where it benefits from the strong intra-cluster amplification of voices and the inter-cluster formation of networked alliances—accompanied by the high visibility of messages from this community—all mean that the Progressive discourse community can articulate its differences with the Hard-Right through active passivity. In other words, this cluster can afford non-engagement with the Hard-Right, and thus achieve two goals: First, by not engaging with the discourse of the Hard-Right, it is articulating its antagonism with the enemy; and, second, it is at the same time silencing and mitigating the voice of the enemy, and thus further sedimenting its hegemonic position in the Australian Twittersphere.

236 Discursive Networks of Articulation The analyses provided in Chapters 6 and 7 provided insights into the aggregate patterns of behaviour on Twitter, and the network structures formed through these collective patterns of articulatory practices enabled by the affordances of the platform. However, there are still unexamined areas in this regard. In these two chapters, I explored the collective behaviours and patterns and the various media objects on Twitter. What is not yet examined in depth, is the content of the messages circulating in each case study. In the next chapter, I move to the textual side of the analysis, providing a discourse-theoretical reading of the tweets posted by the various communities in each case study.

Discursive Networks of Articulation 237 8 Textual Articulations

And it is by no means accidental that this paradox arises at the level of the subject’s relationship to the community to which he belongs: the situation of the forced choice consists in the fact that the subject must freely choose the community to which he already belongs, independent of his choice – he must choose what is already given to him. (Žižek, 2008a, p. 186; emphasis in the original)

8.1 Introduction

Chapters 6 and 7 provided insights into the broad communication patterns, tweeting activities, and discursive formations involved in the three case studies of this project. The findings discussed in Chapter 6 show how different communities draw from discursively resonant information sources, symbolic resources, and crowd-sourced elites (Papacharissi, 2015) to disseminate information about various issues to their followers and the broader

Twittersphere. Chapter 7 discussed how in some cases, however, networked discursive alliances are formed between different communities, in a hegemonic project that forms a horizontally agonistic discursive alliance against a vertical antagonist. Furthermore, Chapter 7 also showed how the material affordances of Twitter, such as retweets and @mentions, are actively and passively rearticulated to amplify antagonisms between the communities across the frontiers in the Australian Twittersphere.

Returning to the theoretical framework of the study, the present chapter moves away from the techno-material and techno-structural aspects of discussions on

Twitter, and shifts the focus of the study to the manifestation of discourses in the texts of the tweets. This aspect of the analysis investigates the themes, topics, and discursive strategies employed by the various discourse communities to internally sediment their discourses, to form discursive

238 Textual Articulations alliances, and/or to amplify antagonisms. Texts, of course, are by themselves treated here as “material surfaces in which discursive practices are inscribed”

(Angermuller, 2014, as cited by Carpentier, 2018); or are, as Fairclough puts it, “written or spoken language produced in a discursive event” (Fairclough,

2013a, p. 95). The primary focus of attention, then, is to investigate how different discourses in the Australian Twittersphere form agonistic networked discursive alliances, while at the same time maintaining their identifications and adversarial antagonisms.

To this end, I first drew from keyword analysis, a technique generally used in corpus linguistics to identify the salient themes and topics in a corpus of texts

(Baker, 2004; Baker et al., 2008). In each case, I compared the smaller corpus of tweets posted by each active community in the study against the larger reference corpus of all tweets in that case study. In order to prevent widely retweeted tweets from skewing the results, I kept only one instance of each tweet, and removed all retweets. Using the log-likelihood measure in the concordance analysis software AntConc (Anthony, 2018), I then calculated the

‘keyness’ of each word in a corpus. The keyness measure is an indication of how unusually frequent (or infrequent) a term is in a particular corpus, compared to the larger reference corpus (Section 4.3.3).

Once the salient themes, topics, and keywords were identified, I moved to a qualitative, in-depth study of tweets containing these keywords. In this process,

I mainly focused on the discursive strategies used to sediment a discourse’s nodal points (intra-cluster dynamics); to form alliances with the Other- adversary or amplify antagonisms with the Other-enemy (inter-cluster dynamics); or to disseminate messages to the broader communication environment (extra-cluster dynamics).

Textual Articulations 239 The following sections provide summative and descriptive findings in each case study. I then synthesise these findings in the last section of the chapter.21

8.1.1 #RoboDebt

In Chapter 7, I discussed how the structures of the networks of retweets and

@mentions in the #RoboDebt case study potentially point to an agonistic space that was formed in the Australian Twittersphere to discuss Centrelink’s automated debt notices. The main active clusters tweeting about the debate were the Progressive and Hard-Right communities. As discussed in Section

7.2.1, almost half of the accounts in each of the two clusters in the network of the Australian Twittersphere (Bruns et al., 2017) contributed to the

#RoboDebt discussion. The third active cluster tweeting about the issue was the cluster of accounts identified as ‘Progressive Political Commentators’ by

Bruns et al. (2017).

I discussed how this community could also be conceptualised as the Hard-Left, given the explicit political positions of the core accounts in this cluster (Section

7.3.2). Using the label ‘Hard-Left’, of course, does not mean that this community necessarily holds Communist or Marxist views. Rather, I use this label to mark the explicitly partisan, left-leaning self-descriptions of accounts in this cluster. For brevity, I now refer to this cluster as the ‘Hard-Left’. In this section, I mainly focus on the keywords used by these communities to investigate whether the agonistic network structures observed in the previous sections are also reflected in the textual representations of the tweets.

21 In the tables providing the top keywords in the discourse of each community throughout this chapter, I have removed non-topical keywords (prepositions, pronouns, and in general, function words), in order to facilitate the reading of the tables.

240 Textual Articulations Investigation of the keywords in the tweets posted by the Progressive Politics cluster—which is also the largest cluster discussing the issue—showed how the

Centrelink controversy is interdiscursively connected to the community’s general position against the incumbent political party in Australia (the Liberal-

National Coalition). In general, the blame for the automated debt notices is directly put on the government and Liberal-National politicians. Furthermore, another salient feature in the discourse of the Progressive cluster—in line with the findings of Rogstad (2016)—is theirreliance on the agendas and terms set by the mainstream media. The term ‘debacle’, for instance, is among the top keywords used in the tweets that discuss the issue. This term was used in one of the earliest news articles published about the debt notices (Martin, 2017).

Although I removed duplicate tweets that shared the headlines of the news articles, and kept only one version of highly retweeted tweets, this keyword remained in the top keywords employed in the original tweets posted by the

Progressive cluster in the first few months of the discussions of #RoboDebt.

Finally, with regard to other keywords used by this community, and the tweets containing them, it is evident that the Progressive discourse community mainly perceives and frames the issue from a political perspective, rather than from a welfare perspective alone. Additionally, the Other-enemy in this discourse is constructed as the government, with the Progressive community framing the debate in such a way that the controversy is not perspectivised as simply the result of a technical glitch or mistake, due to a lack of human oversight. Rather, the issue is framed as a concerted and calculated attack on welfare recipients by the Australian government. Among the list of the top keywords, discursive strategies of nomination and attribution (Fairclough, 2013b; Reisigl & Wodak,

2005) frequently refer to actors such as Alan Tudge, LNP (Liberal National

Textual Articulations 241 Party of Queensland), the government, and Malcom Turnbull, as agents responsible for the controversial automated debt notices.

Table 8-1: Top keywords of the Progressive cluster in the #RoboDebt case

Rank Keyness Keyword 3 221.16 tudge 4 208.89 debt 5 191.86 lnp 8 110.09 centrelink 9 99.44 govt 11 80.54 story 12 64.12 robo 13 62.78 turnbull 14 51.91 debacle 16 51.01 debts 17 45.28 alan 18 44.42 labor 22 37.67 recipients 24 36.92 scandal 27 33.52 victims 28 33.27 staff 33 31.91 fiasco 34 31.71 welfare 35 31.21 alp 37 27.9 inquiry 38 27.7 coalition The other community actively tweeting about the Centrelink controversy is the Progressive Political Commentators, or as I conceptualise them, the Hard-

Left. This community, on the whole, is not as active as the Progressive cluster in tweeting about the issue; however, some of its accounts—such as

@Asher_Wolf—are among the key accounts in the curation of the conversations, and in setting the agendas for the Twitter conversations (Section

6.5.1.1).

The investigation of keywords posted by this cluster showed that although this community is generally perspectivising/framing (Reisigl & Wodak, 2005) the issue in the way that its larger counterpart cluster—the Progressive Politics discourse—does, there are some differences in the discourses of these two

242 Textual Articulations communities. First, tweets posted from the users in this cluster put the direct blame on Centrelink, and not the government. Second, there is a stronger focus on the technical aspects of the data-matching algorithms used by Centrelink, in that the users posting about the issue point to the potential errors and mistakes made in the creation of the algorithm. Finally, there is generally a more affective tone to the tweets posted by the discourse of the Hard-Left, emphasising aspects of the issue that are more likely to invoke negative feelings, rather than mere facts. As a case in point, the official label used to refer to the data-matching algorithm in government documents—Non-Employment

Income Data Matching (NEIDM)—is often framed by activists in this cluster in such a way as to interdiscursively juxtapose it with the labelling conventions used in the military. Often referring to the program as ‘project NEIDM’, they are invoking code name conventions that are used in the military to refer to operations and projects. In one such convention, the noun (operation/project) is often used before the label acronym rather than after it (i.e. operation/project CODENAME, rather than CODENAME operation/project).

Personal and emotive accounts of welfare recipients’ stories and experiences regarding the debt notices also form an important part in the discourse of this cluster. Following such accounts, there are frequent calls and invitations for different rallies and campaigns in Australian cities in protest against the government operation. On the whole, however—with the exception of highly active users such as @Asher_Wolf, who played a central role in the Twitter discussions of the #RoboDebt controversy—this community does not show a high level of activity.

Textual Articulations 243 Table 8-2: Top keywords of the Progressive Political Commentators (Hard-Left) cluster in the #RoboDebt case

Rank Keyness Keyword 1 208.54 gvt 3 153.17 debts 5 103.91 ppl 7 97.34 data 8 78.63 matching 11 60.34 dhs 15 50.36 rally 16 46.23 neidm 17 45.43 campaigns 20 34.18 project 21 33.95 melbourne 23 32.53 centrelink Finally, moving to the discourse of the Hard-Right community, a number of patterns are interesting. First and foremost, there is a high number of keywords which, at first sight, do not seem tohave any relevance to the #RoboDebt controversy. Terms such as ‘polygamy’, ‘wives’, ‘Muslim’, and their various derivatives are among the top keywords in the tweets posted by this community. Upon further investigation, however, I found that these tweets are mainly an artefact of the term ‘Centrelink’, which I used for data collection.

These Islamophobic tweets, in general, make the argument that a large number of Centrelink’s welfare recipients in Australia are Muslims, who illegally practice polygamy and abuse the welfare system by receiving separate welfare payments for each of their wives. In a small number of such tweets, however, there are interdiscursive connections made between this fallacious argument and #RoboDebt, where tweeters argue that the automated debt notices were initially designed to detect such welfare frauds. My close reading of the tweets showed that the majority of such Islamophobic tweets, however, contain only the term ‘Centrelink’, without referring to the automated debt notices and the

#RoboDebt controversy. Given that these tweets did not directly discuss the

244 Textual Articulations issue at hand, I treated them as noise, and did not draw conclusions based on them.

Table 8-3: Top keywords of the Hard-Right cluster in the #RoboDebt case

Rank Keyness Keyword 1 328.89 polygamy 2 260.98 wives 3 198.01 muslim 4 181.38 multiple 5 122.79 centrelink 7 109.22 australia 8 81.04 muslims 11 61.68 dhs 12 61.26 polygamous 13 61.26 spousal 15 55.98 moslems 16 55.13 illegal 17 54.06 taxpayers 18 53.98 benefits 23 43.75 islamic 24 42.22 marriage 25 42.14 welfare 35 33.83 payments However, when moving the attention to tweets that directly discuss the

#RoboDebt controversy, the discourse of the Hard-Right community does not show any divergent patterns from the other communities discussing the issue.

One important observation in this regard, however, is that there were far fewer original tweets posted by the Hard-Right community in discussing the

Centrelink controversy; rather, this community extensively used retweets as a form of endorsement of tweets posted by other communities, especially the

Progressive Politics cluster (as shown in Section 7.2.1).

8.1.2 Section 18C

As discussed in Chapters 6 and 7, the two major communities discussing

Section 18C are the clusters of Progressive and Hard-Right politics in the

Australian Twittersphere. The top keywords present in the discourses of these

Textual Articulations 245 two communities are strikingly different.Although both communities discuss the same issue, each approaches the discussion from a different angle, drawing from different symbolic resources, and seeing different actors as the Other- enemy.

For the Hard-Right community, for instance, a number of major frames form the nodal points of the discourse, in that all discussions of Section 18C of the

Racial Discrimination Act in Australia are perspectivised in light of these signifiers. As can be seen in Table 8-4, among the list of the top keywords, terms such as ‘students’ and ‘QUT’ refer to the framing of the debates around

Section 18C as being directly related to the controversial and high-profile court case (Section 5.3). A reading of the tweets containing these words showed how the court case was frequently used by the discourse of the Hard-Right to indicate flaws in the way 18C cases are handled. This reading constructed a premise that leads the argumentation process to the conclusion that because of these flaws, Section 18C needs to be repealed altogether. The presence of deontic modal verbs such as ‘must’ and ‘should’ in the list of the top keywords by the Hard-Right also shows their strong stance against 18C (Fairclough,

2013b, 2013a). Tweets containing these modal verbs are almost exclusively calls to repeal Section 18C, where these calls are either juxtaposed with the Cindy

Prior vs. QUT court case or with the second major frame in the discourse of the Hard-Right: Islam.

Terms such as ‘Islam’, ‘Islamic’, ‘blasphemy’, and ‘Quran’ are also among the top keywords used in the discourse of the Hard-Right in the debates over whether Section 18C should be repealed or not. A close reading of the tweets containing these terms showed that they make interdiscursive references to a number of issues, all coming together under the discussions of Section 18C.

246 Textual Articulations As a floating signifier of Othering for the discourse of the Hard-Right (Evolvi,

2017; Hafez, 2014; Hogan & Haltinner, 2015), the concept of Islam is frequently invoked interdiscursively to show why 18C must be repealed. This is done in a number of ways. First, a line of argumentation in the tweets containing such keywords is that the Australian Labor Party (the ALP) is pushing to keep 18C intact in order to appeal to their Muslim electorate base and win the next election. It is worth noting that within the Australian political sphere, the

Labor party is generally considered a centre-left political party, and is therefore not a favourite of the Hard-Right discourse in the country. In interdiscursively connecting the ALP with the notion of Islam, this community is strategically achieving a two-fold purpose. On the one hand, and at a broader scale, it internally amplifies its discourse, distancing itself from the discourse of the

ALP, and further sedimenting its identity. On the other hand, it is using the anti-Islam, anti-ALP antagonism to call for changes to, or repealing of Section

18C.

Another line of argumentation put forward by tweets containing Islam-related keywords is that because Islam—according to the Hard-Right’s assertion—is a religion that is fundamentally against freedom of speech, one must be free to criticise it, just as one is currently free to criticise Christianity and/or other religions. This fallaciously constructed premise then leads to the conclusion that the protection of 18C deprives removes the freedom to criticise Islam and, therefore, 18C must be repealed.

Finally, regarding the amplification of antagonism, the discourse of the Hard-

Right positions itself in direct opposition to, and antagonism towards a number of social actors in the Australian socio-political sphere. A number of keywords, and their corresponding tweets, serve to construct a direct inimical antagonism

Textual Articulations 247 against the ‘Labor’ party, the ‘left’, and the ‘AHRC’ (Australian Human Rights

Commission). These referential discursive strategies of Othering are often juxtaposed with predication and attribution strategies (Reisigl & Wodak,

2005), employing pejoratives such as ‘social justice warrior (SJW)’, ‘lefty’,

‘snowflake’, ‘politically correct (PC)’, and ‘regressive left’. Together, these help the discourse of the Hard-Right to create an inimical antagonism between themselves and a collective Other-enemy. By contrast, the keywords in the discourse of the Progressive community, and the tweets containing them, point to the presence of very different ymbolics resources (Table 8-4). However, although the discourse of the Progressive community does not seem to share many signifiers with that of the Hard-Right, the discursive strategies employed by both communities are quite similar.

With regard to the Othering strategies and the discursive construction of the collective antagonist, the Progressive discourse frequently refers to the then prime minister Malcolm Turnbull, his political party (the Liberal Party), the

IPA (Institute of Public Affairs), Andrew Bolt, and the Murdoch news conglomerate. In this way, the Progressive discourse is positioning itself in direct inimical antagonism with the conservative discourse and social actors, blaming them for the calls to change Section 18C. This Other-enemy is then frequently predicated with pejoratives and negative attributes such as ‘right- wing(er)’, ‘bigot’, ‘RWNJ’ (right-wing nutjob), and ‘racist’.

Table 8-4: Top keywords of the Progressive and Hard-Right clusters in the 18C case

Progressive Hard-Right Rank Keyness Keyword Rank Keyness Keyword 1 270.91 lnp 1 782.51 students 2 261.89 ipa 2 613.76 leak 3 236.61 turnbull 3 599.73 qut 6 93.32 changes 4 533.26 bill 7 92.84 right 5 444.86 reform 12 76.02 malcolm 6 442.86 petition

248 Textual Articulations 17 69.53 libs 7 415.48 must 18 36.98 white 8 372.4 islam 21 63.5 leyonhjelm 9 350.6 triggs 22 62.92 murdoch 10 323.72 case 24 59.96 david 11 269.18 quran 25 58.42 liberals 13 244.35 violations 32 49.8 rwnjs 15 240.08 should 33 47.91 rw 16 235.71 repeal 37 43.53 hate 17 234.49 blasphemy 38 43.18 liberal 18 233.47 prior 41 41.86 agenda 23 188.83 ahrc 43 40.42 govt 25 187.44 sack 45 39.03 rwnj 27 182.08 scrap 47 37.17 dutton 29 170.74 cindy 48 36.87 newscorpse 31 168.64 left 49 35.11 racists 41 134.37 pc 50 34.75 bigots 45 127.51 labor The Progressive community interdiscursively re-articulates the discussions of

Section 18C with a frame completely different from that of the Hard-Right.

While for the discourse of the Hard-Right, the framing revolves around freedom of speech, Islam, and the QUT court case, the Progressive discourse perspectivises the debate from the frame of hate speech. The 18th top keyword in the discourse of the Progressive community is the term ‘white’. Tweets containing this term employ predication discursive strategies (Reisigl &

Wodak, 2005) to typify politicians who call for changes to Section 18C as ‘old white Anglo-Saxon males’. This attribution is then followed by the line of argument that repealing Section 18C could potentially pave the way for ‘bigots’ and ‘racists’ to freely participate in hate speech. This frame also recontextualises one of the previous attempts of the Australian government to make changes to Section 18C. In 2014, George Brandis—the then Attorney-

General of Australia—argued that “people have the right to be bigots”, and announced the government’s plans to repeal Section 18C (Chan, 2014).

Overall, in the case of the discussions relating to Section 18C, the two antagonists do not seem to share any common signifiers around which they can

Textual Articulations 249 discuss the issue. Rather, the very topic of the debate—Section 18C—has been transformed into a floating signifier. Through the use of this signifier, both communities amplify their antagonism, while at the same time further sedimenting their discourse internally. Both horizontally and vertically, the allies, signifiers, and discourses are so different that no possibility of deliberation, conversation, debate, or agonism is actualised. The two discussants of the debate are horizontally in a state of inimical antagonism.

Vertically, the Hard-Right discourse positions itself against an Other-enemy with left-leaning and progressive views; meanwhile, for the progressive community, the vertical Other-enemy is the incumbent political party and its politicians. Therefore, the communities do not have, either horizontally or vertically, a common antagonist against which to form a chain of equivalence.

8.1.3 Immigration

The previous two chapters indicated how the discussions about immigration in the Australian Twittersphere form a multipolar discursive space in which a variety of issues are debated from different perspectives by numerous discourse communities. In Chapter 7, the findings of the network analysis pointed to the presence of networked discursive alliances between some of these communities, and an antagonistic frontier between these alliances and the discourse of the

Hard-Right. In this section, I examine the discourse of each of the active communities to investigate the textual manifestations of these discursive alliances and antagonisms.

8.1.3.1 Charities and Human Rights

The community of Twitter users formed around the discourse of human rights is generally concerned with issues related to the health and wellbeing of the refugees in the offshore detention centres and elsewhere. Keywords such as

250 Textual Articulations ‘health’, ‘wellbeing’, ‘women’, and ‘young’, are among the top keywords in the discourse of this cluster. A qualitative examination of the tweets containing the top keywords showed that for this community, the primary issue is that of the necessity for the humane treatment of refugees, asylum seekers, and displaced people, both in Australia and around the world. In general, this community does not necessarily take an explicitly normative position in their tweets regarding what should be done about specific issues such as the offshore detention camps or immigration policies. Rather, the primary frame of discussion for this community is the physical and mental wellbeing and health of refugees, whether they are in Nauru and Manus, among the displaced

Rohingya population, or elsewhere. What is amplified frequently in the discourse of the Human Rights cluster is the calls to support the health and wellbeing of these people by giving them more and freer access to doctors, medical centres, and health services.

Table 8-5: Top keywords of the Charities cluster in the Immigration case

Rank Keyness Keyword 1 1155.75 health 2 841.82 refugee 3 563.02 women 6 293.08 community 7 272.05 australia 8 268.83 young 9 246.05 rohingya 10 231.23 wellbeing 11 196.08 bangladesh 14 184.47 doctors 15 178.13 support 17 162.36 children 18 156.78 camp 20 154.66 dr 26 140.15 mental

Textual Articulations 251 8.1.3.2 Writers and Literature

For the discourse community of Writers and Literature, the issue is again perspectivised from frames revolving around their topics of interest. There are frequent references to books, magazine articles, essays, and various writings by and about refugees and asylum seekers. Additionally, there are calls to invite more writers and literary figures to talk about the plight of refugees in the detention centres. Other than interest-related frames, this community also actively tweets about the situation of refugees in the offshore detention centres, especially with regard to the children still in the Nauru detention centre.

Similar to the discourse of the Human Rights cluster, this community does not engage in extremely positional discussions, and does not formulate a deontic position on what needs to be done about the detention centres or immigration policies. Rather, discussions are generally constrained to news sharing, or to emphasising the negative aspects of detention centres, without direct calls for their closure or for similar measures.

Table 8-6: Top keywords of the Literature cluster in the Immigration case

Rank Keyness Keyword 1 403.76 book 2 316.37 children 3 302.66 nauru 4 260.29 books 5 220.48 writing 8 166.56 literature 10 149.84 writers 11 137.95 immigrant 12 131.31 manus 14 122.2 reading 16 116.01 refugee 17 115.61 libraries 18 108.08 history 19 107.64 novel 20 107.09 tale 22 89.54 magazine

252 Textual Articulations 8.1.3.3 Lawyers and Legal Scholars

Not surprisingly, the community of legal scholars in the Australian

Twittersphere frames the issue from legal perspectives. Unlike some of the other clusters in the dataset, this community takes an explicit position with regard to the offshore detention camps, and calls for their closure. Among the top keywords used by this community are law-related and legal aspects of the discussion, such as ‘rights’, ‘law’, ‘legal, and ‘court’. Tweets containing such terms often argue that the treatment of refugees in the offshore detention camps is against international human rights conventions, and that such practices must stop. For this community, almost all discussions are limited to the offshore detention camps in Australia, and very few references are made to broader discussions of refugees and asylum seekers around the world, or

Australia’s immigration policies in general. Rather, the conversations are very focused on legal aspects of the treatment of refugees in the detention centres in Nauru and Manus.

Table 8-7: Top keywords of the Lawyers cluster in the Immigration case

Rank Keyness Keyword 1 340.75 nauru 2 308.57 refugee 4 200.57 rights 5 194.97 australia 9 149.87 government 12 129.25 law 13 113.67 australian 14 102.45 protection 15 101.09 manus 16 93.84 legal 17 92.33 medical 21 88.15 court 22 86.48 cruel

Textual Articulations 253 8.1.3.4 Politicians and Political Journalists and Journalists

The Politicians and Political Journalists cluster shows a very diverse set of keywords, unlike the interest-related frames and keywords of the other communities. This, of course, is to be expected to some extent since the professional norms dictating the work of journalists generally prevent them from taking an explicit political position, and require them to mainly focus on a balanced sharing of news. This is evident from the keywords used by this community. An investigation of the tweets containing the top keywords in this cluster showed that in general, tweets posted from this community are related to sharing news about the different aspects of the debate.

Table 8-8: Top keywords of the Politicians and Political Journalists cluster in the Immigration case

Rank Keyness Keyword 1 182.85 workers 2 172.38 nauru 3 130.9 olfat 4 119.52 mahmoud 5 117.65 dinner 6 98.81 refugee 8 94.49 proceeds 9 83.03 minister 10 71.85 affairs 11 70.62 committee 12 68.13 chinese 13 64.35 today 15 63.53 says 16 61.13 launch 17 60.9 transcript 19 57.53 iranian 20 57.3 pacific 21 56.15 tonight The same is true for the cluster of Journalists posting about the topic. The keywords posted by this community are also very varied, covering different news about refugees and immigration both in Australia and around the world.

Similarly, this community does not take explicit positions with regard to the

254 Textual Articulations offshore detention centres or immigration policies, and limits their tweets to news coverage.

Table 8-9: Top keywords of the Journalists cluster in the Immigration case

Rank Keyness Keyword 1 738.12 nauru 3 372.88 refugee 4 327.27 pacific 5 308.35 rohingya 7 237.59 australia 9 213.41 bangladesh 10 212.56 forum 11 195.86 says 12 195.65 island 15 172.58 monsoon 16 134.67 story 17 123.66 camp 18 118.97 islands 21 102.72 died 22 99.34 myanmar 23 98.53 oxfam 8.1.3.5 Education and Science

The same patterns of non-positional, general, and interest-related keywords are also present in the discourses of Education and Science clusters. Although both these communities tweeted about the offshore detention centres, these tweets are either related to news sharing about the centres, or frame the discussion from the perspectives of the discourse of the community.

The Education cluster, for instance, frequently tweeted about students with a refugee background, talking about their success stories or how they are, on average, better-performing than students born in Australia. They also emphasise the need for providing education for the children in the Nauru detention centre.

The same pattern is also true for the Science discourse community, which shares success stories about former refugees who have settled in Australia, or

Textual Articulations 255 news about the situation in the Nauru and Manus detention centres. Given the smaller sizes of the corpora in these two communities, and the high similarity of their keywords, Table 8-11 is a combined report of the keywords in the discourse of the two communities.

Table 8-10: Top keywords of the Education and Science clusters in the Immigration case

Rank Keyness Keyword 1 732.7 students 2 376.45 adultlearningau 3 361.23 refugee 4 280.52 school 5 253.75 education 6 174.99 children 7 159.34 teaching 9 143.46 learning 10 131.77 teachers 11 130.24 learners 12 122.56 nauru 13 122.51 today 14 110.01 schools 19 76.55 educators 23 68.46 support 24 67.58 week 28 58.8 scholarship 8.1.3.6 Progressive Politics and Progressive Political

Commentators (Hard-Left)

The two clusters with progressive discourses in the Australian Twittersphere both foreground the issues of offshore etentiond centres in their discussions.

However, unlike the majority of other clusters (discussed above), these communities do take an explicitly critical stance against the offshore detention centres, and their tweets are not limited to the sharing of news about Manus and Nauru detention centres alone.

For the Progressive Politics cluster, the top keywords are generally associated with themes and topics revolving around the situation of refugees and asylum seekers in Australia. Keywords such as ‘Nauru’, ‘Manus’, ‘refugees’, ‘asylum

256 Textual Articulations seekers’, and ‘detention’ are among the top ten keywords in the discourse of this community. However, a review of the tweets containing these keywords, and of other terms among the top keywords employed by this community, pointed to their positionality with regard to the issue. Rather than merely sharing news, the Progressive Politics discourse community frequently and extensively criticises the actions of the Australian government with regard to the offshore detention centres. This community also criticises the opposition party—the ALP—for not taking any meaningful steps to close the camps, and for sometimes even backing the policies of the Coalition and the LNP (Liberal

National Party of Queensland) in continuing the offshore detention of refugees in Australia.

Table 8-11: Top keywords of the Progressive cluster in the Immigration case

Rank Keyness Keyword 1 3191.64 nauru 2 1967.59 dutton 3 1237.24 manus 4 1145.55 refugees 7 1015.75 seekers 8 1000.91 asylum 9 927.62 detention 11 817.21 australia 12 795.37 labor 13 751.6 lnp 15 702.58 refugee 16 665.65 action 17 643.9 alp 19 485.02 medical 21 472.07 seeker 22 437.25 peter 23 396.98 govt 30 339.9 rights 31 334.51 offshore Similar to the Progressive Politics community, the cluster of users in the

Australian Twittersphere that are labelled as ‘Progressive Political

Commentators’ also shows an explicit stance against the offshore detention

Textual Articulations 257 centres. However, as shown in Section 7.3.2, compared to the larger Progressive

Politics cluster, this community is generally more explicit and positional, with many of its users providing explicitly partisan self-descriptions in their profiles.

In this sense, I suggest that this particular cluster of users can also be conceptualised as the Hard-Left. This explicit positionality is also reflected in the tweets posted by this community. Although both the Progressive Politics and Progressive Political Commentator (i.e. the Hard-Left) communities focus on the issue of the offshore detention centres and criticise the position of the government in this regard, they show differing discursive strategies in doing so.

Table 8-12: Top keywords of the Hard-Left cluster in the Immigration case

Rank Keyness Keyword 1 559.35 fuck 2 482.98 agents 3 373.5 captured 7 317.46 nauru 8 279.13 unaccompanied 11 226.14 broken 12 219.82 immigrant 13 219.78 child 15 200.62 mexican 16 172.05 refused 20 129.55 rape 23 101.09 children 24 94.59 asylum 25 79.28 poc 26 75.99 seekers 27 73.16 seeker 29 68.58 manus 30 65.12 ice One major difference between the discourse of the two communities is that the

Progressive cluster almost exclusively focuses on the Australian context, while the Hard-Left cluster focuses on both Australian and international issues related to immigration and refugee policies. Treatment of refugees at the US–

Mexico border by ICE (Immigration and Customs Enforcement Agency), for instance, is a salient theme in the tweets posted by the Hard-Left cluster.

258 Textual Articulations Another major difference between the two communities in the representation of the issues related to immigration is the frames and discursive strategies employed by the two. The Progressive community generally maintains a more rational, yet positional stance, focusing on well-established facts, mainstream media outlets (Section 6.4.3), and Australian political contexts. The Hard-Left discourse, on the other hand, takes a more emotive and affective route, intensifying news that will (expectedly) draw strong emotional responses; for example, news about the rape and sexual harassment of refugees in detention centres around the world. This affective and intensified tone is also reflected in the top keywords employed by this cluster, making the term ‘fuck’ the top keyword in the discourse of this community. My qualitative reading of the tweets containing this term showed that in the majority of tweets, the use of this term adds to the emotive weight of the message; the word is generally used to add emphasis to the anger expressed by the user posting the tweet (as in

‘fucking’ plus adjective, ‘racist fuckwit’, phrasal verbs containing the word

‘fuck’, or offensive tweets aimed at politicians).

8.1.3.7 Hard-Right

The international focus, emotive tone, and intensification of taboo subjects observed in the discourse of the Hard-Left are also salient features of the discourse of the Hard-Right cluster in the Australian Twittersphere. Similar to the observations in the discourse of the Hard-Right with regard to their representation of Section 18C of the RDA (Section 8.1.2), where Islamophobic themes are a salient feature, the same pattern is apparent in their debate over immigration. Among the list of the top keywords employed by this cluster are

(again) Islam-related terms such as ‘Muslim’, ‘Islamic’, and ‘Jihad’.

Textual Articulations 259 A review of the tweets containing these words showed the same interdiscursivity that was employed in the 18C case study. The discussions of immigration and refugee policies in Australia are interdiscursively connected to the concepts of Islamic fundamentalism and terrorist attacks in Europe, and argue that an open border policy that is welcoming to refugees will eventually lead to an increase in the number of terrorist attacks in a country. As pointed out in Section 8.1.2, the signifier ‘Islam’is a nodal signifier of difference for the discourse of the Hard-Right. By interdiscursively connecting a theme to this signifier, the Hard-Right discourse re-articulates its antagonistic position against any discourse that shows any level of inclusion of the notion of Islam, including racial discrimination and immigration debates.

Table 8-13: Top keywords of the Hard-Right cluster in the Immigration case

Rank Keyness Keyword 1 4706.99 muslim 2 2632.09 migrants 3 1708.49 illegal 4 1656.55 germany 5 1590.01 migrant 6 1574.34 illegals 7 1532.15 mass 8 1509.66 europe 9 1396.18 immigration 10 1193.97 sweden 11 1127.2 italy 12 1057.31 german 13 1030.27 eu 14 888.8 merkel 15 778.17 migranten 16 669.76 islamic 17 621.84 swedish 18 502.89 welfare 19 501.2 jihad 20 488.4 watch 21 469.08 paris 22 457.08 spain 23 452.16 free 24 434.15 hungary 25 421.72 african

260 Textual Articulations 26 408.98 police 27 393.14 raped 28 390.44 speech 29 384.37 borders 30 349.39 rape 31 333.84 islam 32 330.62 deport 33 316.14 democrats 34 302.79 invasion Another interesting pattern in the discourse of the Hard-Right in the

Australian Twittersphere is their active avoidance of discussing the offshore detention camps. Unlike all the other clusters investigated in previous sections, where the term ‘Nauru’ was among the top keywords used by all the different communities, the discourse of the Hard-Right does not make any references to the offshore detention camps in Australia. Rather, there is a strong international focus in their tweets, which often discuss the so-called ‘refugee crisis’ in Europe; they connect this crisis to discussions of terrorism, and their support for stronger borders.

Finally, similar to the discourse of the Hard-Left, where affective tones and news with strongly negative emotive connotations are shared, the discourse community of the Hard-Right also relies extensively on the themes of rape and sexual harassment. However, these themes are employed in the exact opposite way to the discourse of the Hard-Left. While for the Hard-Left such news often involves sharing stories about how refugees were raped and/or sexually harassed by the authorities at the borders or detention centres, the Hard-Right shares news about how refugees who are allowed into a country rape or sexually harass the citizens of that host country. Of course, it goes without saying that

I am using a very loose definition of the term ‘news’ here, and that the authenticity and factuality of such news is always a matter of concern, whether shared by the Hard-Left or the Hard-Right communities. Beyond the question

Textual Articulations 261 of whether or not this is ‘fake news’, the more significant point for this study is the way such themes and topics are employed by the far sides of the political spectrum. In this sense, both sides share a common discursive strategy, investing in the negative affective oadl of such news to amplify their antagonisms.

To put the different keywords used by the discourse communities involved in the immigration debate into a broader perspective, Figure 8-1 displays a semantic network of these words. In this network, clusters and keywords are treated as nodes; there are edges between a cluster and all its keywords, and also between words that are common in more than one cluster. As the figure demonstrates, the semantic network of the keywords has a very similar structure to the network of retweets in this case study.

Figure 8-1: Semantic network of keywords in the discourse of communities in the Immigration case Overall, the discourse of the Hard-Right does not have many keywords in common with the other communities involved in the discussion. This is the result of their different framing and symbolic resources; their focus on

262 Textual Articulations discussing the immigration debate from an anti-Islam, anti-immigration perspective; and their strategic silence on the issue of the offshore detention camps in Nauru and Manus.

This chapter showed how the other communities involved in the debate all focus on the issue of refugees in the detention centres, albeit from various perspectives. This common focus, which forms a chain of equivalence between them regarding the immigration debate in Australia, is also reflected in the semantic network of the keywords used by these communities. At the same time, their unique perspectivations, which rely on their attempt to maintain identification with their own community and discourse, also mean that each cluster has a set of keywords unique to its own discourse, separating it from the Other-adversary’s discourse.

8.2 Conclusions

In this chapter, I investigated the manifestations of discursive struggles and their dynamics in the texts of tweets posted by the various discourse communities in each case study. Overall, the analyses provided in this chapter showed how each discourse community draws from its own discourses and symbolic resources to approach, discuss, and re-articulate each issue.

Viewing this in light of the formation of networked discursive alliances, as shown in Chapters 6 and 7, the knotted nature of the discursive and the material in the dynamics of the Twitter discussions becomes clearer. In each case study, agonistic alliances were formed between different discourse communities in direct oppositional antagonism to a horizontal or vertical

Other-enemy. However, each discourse simultaneously needs to maintain its partial fixity and closure. In this way,the Twitter users discussing each issue

Textual Articulations 263 have to rely on the networked discursive alliances to hegemonise their broader discourse, while at the same time, identify with the most resonant discourse

(community). A hypothetical legal scholar discussing Section 18C and protesting changes to it, therefore, has to simultaneously maintain their identification as a legal scholar while forming an alliance with a hypothetical anti-racism activist.

This is the very moment the discursive, the material, and the agency of the

Twitter user come together in a non-hierarchical knotted fashion. Through retweeting the discourse of the anti-racist activist, the legal scholar forms a networked discursive alliance with the activist, built on the material affordance of retweeting. At the same time, when posting an original tweet on the issue, the legal scholar frames the tweet from the most discursively resonant perspective—the legal aspect of the discussion. The activist then retweets the legal scholar, further amplifying the networked discursive alliance. This was also the case in the discussions of the #RoboDebt. As shown, the Hard-Right discourse primarily employed retweets in the formation of a networked discursive alliance with the discourse of Progressive politics, but posted few original tweets when discussing the issue.

Furthermore, the findings ofthis chapter also show how the discursive alliances are formed around contingent, partially fixed discourses and signifiers. For the horizontally agonistic formations observed in the Immigration case study, for instance, the notions of ‘refugees’ and ‘offshore detention centres’, per se, become floating signifiers. These floating signifiers which, as Torfing argues, are “overflowed with meaning” (1999, p. 301), are employed by the various discourses in the networked discursive alliance to further sediment and fix the discursive structures. Each discourse in the case study ascribes its own

264 Textual Articulations meanings to the signifier ‘refugee’, and builds their frames and arguments based on such ascriptions. Similarly, in the 18C case study, Section 18C, per se, turns to a nodal point in the discourses of the Progressive and Hard-Right clusters, with each ascribing it with different meanings and frames.

In such conceptualisation of the networked discursive alliances, the aforementioned role of active passivity and strategic silence is also significant.

As shown in Chapter 7, the more hegemonic discourse of Progressive Politics is generally in a non-reciprocal retweeting relationship with the other communities; that is, while other communities retweet this cluster extensively, the Progressive cluster does not necessarily retweet them back. This non- reciprocity is also apparent in the findings of this chapter. Each of the clusters in the networked discursive alliance generally frames their tweets in their own perspective, drawing from their own symbolic resources. However, all these are done under the broader umbrella discourse of progressive politics. In this way, the less powerful and not-yet-hegemonic discourses form alliances with the more powerful and hegemonic discourse, yet at the same time, maintain their adversarial antagonism to it.

On the other hand, the hegemonic discourse (community) does not need to reciprocate this alliance. Rather, the articulation of alliance is done for it by the other communities. In this light, the very practice of remaining impassive and not interacting because the others are doing so, creates an articulatory relation for the hegemonic discourse through interpassivity (Kappler & de

Querol, 2011; Ruppert, 2011; Žižek, 1998). In such a relation, the act of alliance-formation is done for the Progressive discourse, and this discourse is active through the Other.

Textual Articulations 265 The articulatory practice of active passivity was also shown to be a major discursive strategy for the discourse of the Hard-Right. The investigation of keywords posted by this discourse community showed that in general, this cluster strategically chooses to not engage with the issue of refugees and asylum seekers in the offshore detention camps, remaining silent about the situation in the camps, and the hardships the refugees undergo there. On the contrary, the focus of attention for the Hard-Right is on the ‘kind of people’ who arrive in

Australia by boat. Framing them as illegal immigrants, queue jumpers, terrorists, and non-genuine refugees, the Hard-Right re-articulates the discourse from a preventative frame, rather than a humanitarian one. The end- goal of the discussion for this discourse, then, is to prevent the arrivals, with no regard for what happens once someone arrives and is detained in an offshore detention camp. The strategic silence regarding the offshore camps, therefore, is an articulatory practice formed through active passivity. If one is to discuss the offshore detention campsand the situation of the refugees in them, one has to make a choice between either supporting the inhumane treatment of the residents of the camps, or denouncing it.

In the case of the former choice, the agent risks further marginalisation and loss of discursive power, since they are articulating a position completely against the hegemonic discourses. The second choice is also discursively dissonant with the identification of an agent as a Hard-Right actor. In such a case, then, the most viable discursive strategy remains keeping silent and not discussing the issue at all. This active passivity, or engagement through the negation of interaction with the topic is, therefore, in itself a reflection of the users’ symbolic resources. As various scholars argue, what is not discussed in a discourse—or the question of absence—is often as important as what is

266 Textual Articulations present and discussed in that discourse (see, for instance, Schröter & Taylor

[Eds.], 2018).

Textual Articulations 267 9 Synthesis and Discussion

Don’t matter who did what to who at this point. Fact is, we went to war, and now there ain’t no going back. I mean, it’s what war is, you know? Once you in it, you in it. If it’s a lie, then we fight on that lie. But we gotta fight. (Simon, 2004)

9.1 Introduction

The previous chapters each focused on a set of analytical constructs and sensitising concepts used in this project. In Chapter 6, I investigated the collective and aggregate tweeting behaviours and patterns in each case study, showing how different users and discourse communities react to events, bring new information into the Twitter discussions, and strategically choose their crowd-sourced elites. That chapter showed how the various technological affordances of Twitter are re-articulated in always particular ways, in which users employ them to reproduce and amplify their own discourses, to engage in online activism; and/or to give visibility to different perspectives, users, discourses, and voices.

Moving on from the aggregate metrics, I then investigated the assemblages and clusters created by these collective, yet particular articulations. The analyses in Chapter 7 showed how the collective and particular ways of engaging with the material affordances of Twitter lead to the construction of what I conceptualised as networked discursive alliances that effectively bring together discourses and communities of users in a hegemonic project. Finally, in Chapter

8, I turned my attention to the ways in which each of these communities balances their own particular identifications and networked discursive alliances in a way that ensures the maintenance and sedimentation of their own

268 Synthesis and Discussion discourse, and the formation of a chain of equivalence against an inimical antagonist.

In this chapter (Chapter 9), I synthesise these findings to reach a more in- depth theorisation and conceptualisation of the dynamics of discursive struggles in the Australian Twittersphere. While I have briefly discussed discourse-theoretical interpretations of the findings in their respective chapters, the present section of the thesis brings these together.

9.2 Discursive-Material Articulations

I established previously how the engagement (or lack thereof) of a Twitter user with the different affordances of the platform is, per se, an articulatory practice that creates links between the moments (the user and the affordance) in a way that modifies the identities of the articulators, and the objects enabling that articulation.

In Chapter 6, the analyses of aggregate engagements with the various media objects and affordances of Twitter—such as retweets, hashtags, @mentions, and URLs—showed how different clusters of users in the Australian

Twittersphere draw from discursively resonant information sources and discourses in order to amplify and sediment their discourse internally and vis-

à-vis the broader communication environment (i.e. intra- and extra- cluster dynamics). The analysis of tweets over time, for instance, showed that although the patterns of communications and tweeting activities generally reflect the events in the vertical context of the society, different communities react to different events based on their discursive identifications (Section 6.2.2). The qualitative analysis presented in Chapter 8 further supplemented this finding by showing how such reactions are, simultaneously, mirror images of a

Synthesis and Discussion 269 community’s discursive identifications. That is, while two communities might show a similar level of reaction to certain breaking news, this does not necessarily mean that they show similar reactions. Each discourse articulates their reaction using their own set of symbolic resources and nodal points in the discursive formations in which they operate.

I now use the example of hashtags—as a primary communicative affordance of

Twitter—to elucidate these discursive-material articulations. In Chapter 6, the findings suggest that although the primary, topical hashtags relating to a discussion are used extensively by users to mark the topicality of their tweets— essentially creating issue-based ad hoc hashtag publics (Bruns & Burgess,

2015)—different communities employ their own sets of secondary hashtags in the discussions of different topics. In this sense, the use of a hashtag by a

Twitter user creates an articulatory relation not only between the user and the hashtag, but also between the primary and secondary hashtags in a tweet; between the user and any other user who employs the two hashtags together; and between the user, the hashtags, and any other user who sees those tweets and hashtags on Twitter. This complex network of articulations within articulations, therefore, establishes assemblages of moments, in which the identity of each moment is contingent and dependent on how different users ascribe meanings to it in this network.

This abstract conceptualisation can be illustrated in a concrete example.

Consider, for example, the use of a particular topical hashtag (e.g. #18C) within a tweet, juxtaposed with a secondary hashtag (e.g. #Islam). Here, the network of articulations within articulations (conceptualised above) could be seen in the link that the use of these hashtags creates between the account posting the tweet and the hashtags themselves. Through using the two

270 Synthesis and Discussion hashtags together, the user articulates their identity (e.g. as a Hard-Right user), further sedimenting their identification with the Hard-Right discourse.

At the same time, an articulatory relation is created between the two hashtags, interdiscursively linking the discourse of Section 18C with the discourse of

Islamophobia. In this way, the limits of the discursive field in which discussions can take place are further modified bythe introduction of a new discourse to the discussion of Section 18C.

For a user following the discussions (e.g. by reading tweets containing the hashtag #18C), an articulatory relation is established once they read such a tweet. Upon seeing the tweet, the agency of the individual, and the discourses within which they are situated, come into play in forming their eventual decision: Whether to use the two hashtags together in their own tweets, and further sediment this articulation and significations associated with it, or whether to take an antagonistic position and resist the chain of equivalence created through the joint articulation of #18C and #Islam. In facing such a decision, the identity of the user is also placed in an articulatory relation with the hashtags; the tweets in which the hashtags are used; the user posting the hashtag; and the discourse in which the original poster is operating.

Figure 9-1 conceptualises such articulatory relations between three users (A.1,

A.2, and A.3) and the two juxtaposed hashtags within a tweet (H.1 and H.2).

In the diagram, the articulatory relation between A.1 and A.2 is that of endorsement and sedimentation of the discourse in which H.1 and H.2 are interdiscursively linked; meanwhile, A.3 resists such articulation by avoiding the combined use of the two hashtags.

Synthesis and Discussion 271

Figure 9-1: Conceptualisation of multilayered discursive articulations Figure 9-1, of course, uses the example of hashtags as one of the most obvious examples of communication on Twitter. The same articulatory relations and practices take form within any other connections and simple networks afforded by the materialities of Twitter, such as following others, retweets, @mentions,

@replies, and so forth. In any such relations, therefore, the multilayered discursive articulation happens between the users (accounts), the affordance, and the meanings ascribed to the affordance.

It is worth noting that the above conceptualisation itself does not occur in a vacuum, isolated from the structural dimension of the discursive-material knot.

The decision of a user to articulate their identification with a certain discourse and, in turn, with the hashtags and other users using them, is also intertwined with the structural aspects of both vertical and horizontal contexts. While the above schematic presentation of articulatory relations focuses on only one occurrence of such articulation, the role of the technological design of the platform and the articulatory practices of other users in the formation of collective articulatory practices, is undeniable.

272 Synthesis and Discussion Most importantly, the role of what is known as ‘contagion’ in social network analysis is particularly significant in this regard. A user facing the joint articulation of #18C and #Islam for the first time, for instance, might not necessarily be influenced to jointly incorporate them in their tweets. However, if they observe the same pattern repeatedly, they might be more likely to do so. This can become even more pronounced if the user observes the pattern as originating from several accounts in their network, rather than from a handful only. This process of complex contagion (Centola & Macy, 2007) is well- documented in studies of social networks (Romero, Meeder, & Kleinberg, 2011).

Although exposure to information alone often follows a simple contagion model—in which only one exposure is enough to transfer the information from one node to the other—it generally takes several exposures from several different nodes to influence a user’s decision to accept that information

(Centola & Macy, 2007). This is where the role of the horizontal context of the platform becomes more pronounced, especially with regard to the issue of the structures of the network in which a user is situated, and the role of automated accounts and bots.

A user in a cluster of accounts that are more likely to articulate the two hashtags together becomes more likely to be exposed to this articulation and, therefore, more likely to adopt this articulation. Similarly, if the social network of the user contains a critical mass of bots that frequently articulate these hashtags together, the user is arguably more likely to be influenced by this articulatory practice. This further sediments the discursive articulation of

#18C with Islamophobia, for instance. Conversely, another user, whose social network is more likely to make the decision not to jointly articulate the two hashtags, is more likely to also make the same decision, and take the route of negative articulation and passivity. This, in turn, can create a “spiral of

Synthesis and Discussion 273 silence” (Noelle-Neumann, 1974), which further sediments the practice of active passivity when faced with the discourse of the Other. (I turn to this in more detail in Section 9.4.)

The same complex network of articulatory practices can be conceptualized for the other affordances of Twitter. Articulatory relations can be seen in the acts of retweeting, @mentioning, sharing URLs, @replying, following, and the like.

In the same way as the conceptualisation of hashtags discussed in the previous paragraph, these articulatory relations are never only between two users, or a user and a media object. Rather, they are always in a complex, multilayer articulatory network, within which the identity of all moments is always contingent on the identification of all other moments. In the act of retweeting, for instance, the moments and discourses could be conceptualised as the original poster of the tweet; their discursive field; the tweet itself; the affordance of retweeting; the meanings ascribed to the act of retweeting; the user doing the retweeting; their discursive field; any user seeing the original or retweeted tweet; and their decision to tweet or not.

Within the horizontal context of a social media platform such as Twitter, therefore, the knotted nature of the discursive and the material becomes clearer. In this field of discursivity, the moments are not only the textual and/or multimodal signifiers used in thetexts of the tweets: The various media objects, affordances, and all the possible relationships between them also become moments in the assemblages of the technological affordances of

Twitter, its users, and their material representations as profiles on the platform. In this assemblage, the non-hierarchical inter-relationship of discourse, structure, material, and agency is manifest. For a user on Twitter, who is faced with a surplus of information and plurality of discourses on and

274 Synthesis and Discussion beyond the platform, agency comes into play in their identification with a particular discourse. As Carpentier (2017, p. 68) explains:

Agency impacts on the discursive through the exercise of identificatory choices, where in a context of discursive plurality the subject can align herself or himself through the logic of identification with different discourses in always different ways.

Additionally, upon facing the discourse of the antagonistic Other, individuals are faced with a number of alternative discursive practices and strategies: to ally themselves with the discourse of the Other and/or endorse it (i.e. form a chain of equivalence and adversarial antagonism; to remain silent and ignore it (i.e. to form a chain of difference through non-engagement); or to react negatively to it (i.e. to form a chain of difference through antagonistic engagement). Such discursive practices are themselves always dependent on the broader structures of society and the platform. At the same time, these practices depend on the affordances of the platform, whether through a positive engagement with them—such as retweeting or @mentioning—or a negation of engagement with the discursive and the material (for example, by keeping silent).

The material affordances of Twitter, in this sense, do not possess intrinsic meanings. Rather, they are ‘relational properties’ (Bucher & Helmond, 2018), empty signifiers in need of being discursified. In each individual instance, a

Twitter user ascribes meaning to a particular affordance, by discursifying it through taking one of the alternative choices extended to them by the materialities of the affordance (e.g.positive engagement, negative engagement, or non-engagement). Returning to Carpentier’s conceptualisation of ‘invitation’

(2017, p. 45), we are reminded that

Synthesis and Discussion 275 materials extend an invitation to be discursified, or to be integrated in discourse, in always particular ways. These invitations, originating from the material, do not fix or determine meanings, but their material characteristics still privilege and facilitate the attribution of particular meanings through the invitation.

The attribution of particular meanings that Carpentier refers to above could be understood as being directly related to two key factors: first, the individuals’ agentic choices and their embeddedness in different discourses play a significant role in their ascription of particular meanings to different affordances of

Twitter; and, second, the fact that these meaning ascriptions are also dependent and influenced by the set of possible meanings that the affordance privileges and facilitates. These privileged meanings are what eventually leads to the different ways that users employ and (re-)appropriate (Hayes, Carr, &

Wohn, 2016) the affordances of Twitter. What the individual eventually chooses to do once they see a tweet, therefore, manifests itself in a discursive- material knot, as an assemblage that brings together the individual’s agency, discourses, and the materialities of the platform.

9.3 Networked Discursive Alliances

Following the logic of the previous section, it can be argued that once a large number of individual users articulate and discursify the affordances of the platform in particular yet similar ways, this will lead to the construction of assemblages of techno-discursive formations (clusters), comprised of various moments such as the technological affordances of the platform, the profiles of the users, media objects, and so on. These articulatory clusters, therefore, are material inscriptions of discursive meanings on the platform. Although the discursive, in its abstract sense, cannot be empirically ‘seen’, its material manifestation on Twitter leaves ‘digital traces’ (Venturini & Latour, 2010) that could be collected, analysed, and visualised. This logic formed the backbone of

276 Synthesis and Discussion the analyses provided in Chapter 7, where I used social network analysis to investigate the dynamics of such discursive formations; the flow of information within and between them (intra- and inter- cluster dynamics); and the different discursive struggles manifested in them.

The different network structures formed in the three case studies of this project

(Chapter 7), and the qualitative analysis of tweets posted by the various communities involved in each case (Chapter 8), led to the theorisation of the concept of ‘networked discursive alliances’. These networked discursive alliances were shown to be formed in the construction of both agonistic and antagonistic spaces. In the discussion of the #RoboDebt case study, for instance, it was shown how the antagonistic discourse communities in the

Australian Twittersphere formed a discursive alliance. This alliance was manifested in the material affordanceof retweeting, in order to create a horizontally hegemonic project against a vertical antagonist that had unfairly issued automated debt notices. In the two other case studies, these networked discursive alliances were formed at the horizontal antagonistic frontiers between the Progressive and Hard-Right discourses. At the same time, the vertical antagonistic frontiers also influenced the formation of such agonistic and antagonistic spaces: The antagonistic discourses formed alliances not only against each other—in the horizontal context of the platform—but also against vertically antagonistic Other-enemies.

In the construction of the networked discursive alliances, I also showed how the different discourses forming an alliance in a chain of equivalence generally depend on a more established, hegemonic discourse as the discourse that brings them together. In the three case studies (more so for the 18C and Immigration studies, and less so for the #RoboDebt), this discourse was found to be the

Synthesis and Discussion 277 cluster of Progressive Politics in the Australian Twittersphere. The analysis of retweeting and @mentioning patterns among the different discourse communities, and the qualitative examination of the tweets posted by each of them, showed that it is generally the non-hegemonic, smaller discourse communities that join the Progressive community to construct a chain of equivalence in a hegemonic project. Laclau theorises this as the reliance of discourses in a chain of equivalence on the presence of a link that can

“condense” all the others in an equivalential chain (Laclau, 2005, p. 100):

But if—given the radical heterogeneity of the links entering into the equivalential chain—the only source of their coherent articulation is the chain as such, and if the chain exists only in so far as one of its links plays the role of condensing all the others, in that case the unity of the discursive formation is transferred from the conceptual order (logic of difference) to the nominal one.

In another work, Laclau presents this relationship in a diagrammatic way, showing how a particular discourse becomes the condensing link in a chain of equivalence against an antagonist (Laclau, 2000, p. 303)22.

Figure 9-2: Laclaus diagrammatic presentation of chains of equivalence

[W]here T stands for Tsarism (in our example); the horizontal line for the frontier separating the oppressive regime from the rest of society; the circles D1 . . . D4 for the particular demands, split between a bottom semi-circle representing the particularity of the

22 Laclau uses an equal sign (=) in his diagram to show the equivalential relations. However, Thomassen (2005) argues that since the intended relation is that of equivalence and not identity, a triple bar (Ł) is more appropriate.

278 Synthesis and Discussion demand and a top semi-circle representing its anti-system meaning, which is what makes their equivalential relation possible. Finally, D1 above the equivalent circles stands for the general equivalent (it is part of the equivalential chain, but it is also above it).

To map Laclau’s conceptualisation (above) onto the findings of this study, the different demands in Laclau’s example (D2 to D4) are the smaller, non- hegemonic discourses that formed—through retweets and @mentions—a networked discursive alliance with the discourse of progressive politics (D1), against their horizontal and vertical antagonists (Tsarism in Laclau’s example;

Hard-Right discourse and politicians in this project).

In parallel to the concept of networked discursive alliances, the qualitative analysis presented in Chapter 8 also showed how the different discourses in the equivalential chains maintain their identifications with the most resonant discourses. In the construction of networked discursive alliances, communities of users often rely on the material affordances of retweeting and @mentioning to construct the agonistic space needed in the hegemonic project. However, at the same time, when producing texts of their own to further feed the agonistic space, they draw primarily from the most resonant discourses. This was evident in the range of discursive strategies employed by the different clusters. The analysis in Chapter 8 showed that in line with Reisigl and Wodak’s (2005) arguments, the issues, actors, and discourses foregrounded and intensified by each discourse community were generally those that were the most resonant with the community. At the same time, dissonant issues were mitigated, in order to avoid discursive dissonance (Section 8.2).

In such a way, communities create the required agonistic alliance by engaging with the discourse of the Other-adversary through retweets (shown as straight solid arrows in Figure 9-3). At the same time, however, they are reproducing

Synthesis and Discussion 279 and amplifying their own identifications through the production of particular discourses in their original tweets (shown as circular arrows in the diagram).

This further enriches and expands the limits of the discursive formations in which a particular issue is discussed.

Figure 9-3 is a schematic of the formation of networked discursive alliances. In such a formation, each discourse reproduces its own signifiers, and draws from its own nodal points; at the same time, it forms a chain of equivalence with the hegemonic discourse, mainly through the use of the material affordances of

Twitter (such as retweeting and @mentioning).

Figure 9-3: Conceptualisation of networked discursive alliances Figure 9-3, in this sense, can be viewed as an expansion of Laclau’s conceptualisation of chains of equivalence (Figure 9-2), especially with regard to its manifestation on Twitter. In Laclau’s theorisation, each demand

(discourse community) has to maintain its own particularity while joining the general equivalent. As the different case studies of this project showed, within the context of the Australian Twittersphere, this process often manifests itself in the form of an engagement with the material affordances of the platform, and with original tweets. The smaller circles on the left and right sides of the diagram (marked as D2 to D5 on the left and D2́ to D́4 on the right) each

280 Synthesis and Discussion represent one of the discourse communities on either side of the antagonistic frontier. While each smaller discourse community reproduces its own discourse through original tweets and internal retweeting (represented as circular arrows), they join the larger hegemonic discourse within their discursive field— shown as D1 and D1́ —through extensive engagement with it, mainly through retweeting and @mentioning.

On the two sides of the antagonistic frontier, therefore, one can see various demands (discourse communities) constructing a chain of equivalence through networked discursive alliances. On the left side, for instance, we can conceptualise the discourses of Legal Scholars, Activists, Hard-Left, and international progressive politics communities (D2 to D5), all joining the hegemonic discourse community of Progressive Politics (D1) in the Australian

Twittersphere. Similarly, on the right side of the diagram, a networked discursive alliance is formed among the ultra-conservative users, the international far-right, right-wing discourses (D́2 to D́4), and the Hard-Right community (D1́ ).

I showed how each of the discourse communities involved in the different case studies of this project are generally aware of, and exposed to, the discourse of their Other-enemy. Additionally, the investigation of the intra- and inter- cluster flows of information showed that in spite of this awareness and exposure, inter-cluster interactions are not necessarily reciprocal. While the smaller discourse communities retweet and @mention the hegemonic ones, this generally remains a non-reciprocal interaction. Similarly, while the counter- hegemonic discourse of the Hard-Right actively engages with its Other-enemy, the Progressive Politics cluster actively avoids interaction with its inimical antagonist. This is presented as solid arrows from the right to the left, and a

Synthesis and Discussion 281 dashed line from the left to the right of the diagram. What the dashed line emphasises, therefore, is that the absence of interaction between the two poles of the antagonistic frontier is not due to a lack of exposure (i.e. to filter bubbles and/or echo chambers), but to active passivity towards the Other-enemy. I discuss each of these in more detail in the following sections.

In the formation of networked discursive alliances, therefore, the material affordances of Twitter find their discursive significance when seen in light of their role in the dynamics of discursive struggles, and in the construction of temporary spaces on the platform in which antagonism and/or agonism can manifest. While, as discussed in Chapter 3, the more permanent discourse communities were formed through follower–followee relationships (Section 3.7), the investigation of links within, between, and among these communities (see

Chapter 7) showed that the formation of networked discursive alliances sometimes transcends the more permanent relationships between them.

The ad hoc issue publics (Bruns & Burgess, 2015); the ‘momentary connectedness’ (Rathnayake & Suthers, 2018) created due to the collective use of hashtags; or the networks of discursive alliances formed through collective retweets and @mentions act as temporary spaces within which antagonisms and agonisms manifest. In such temporary spaces, each discourse community has to maintain a balance between their own identification and their alliance with the other discourses. That is, any formation of a networked discursive alliance brings a (temporary) dislocation to the discourse, through modifying its limits by connecting it to another discourse. In a struggle between maintaining the partial closure of the discourse—which is temporarily dislocated by introduction of new moments—and the formation of an agonistic

282 Synthesis and Discussion space, discourse communities discursify the material affordances of Twitter in different ways.

As shown in Chapter 7, the affordances of retweeting and @mentioning play a significant role in the creation of networked discursive alliances. By strategically discursifying these affordances, a discourse community does not necessarily need to permanently incorporate new signifiers and perspectives in their tweets; indeed, doing so, can potentially dislocate the discursive formation. Rather, through retweeting an ally, they endorse the message and bring the discourses closer together. In parallel, the maintenance of identification and partial fixity of thediscourse is primarily achieved through the reproduction of a discourse’s own frames and perspectives in the original tweets (as shown in Chapter 8); these are then likely to be retweeted by the other discourse communities in the networked alliance.

Within the cluster, therefore, intra-cluster dynamics manifest themselves through the reproduction of discourse and sedimentation of signifiers, mainly achieved through original tweets. In parallel, inter-cluster dynamics of agonism and antagonism are mainly manifested through retweets and @mentions. In such a way, communities can maintain their identification with the most resonant discourse (intra-cluster dynamics), form alliances with other resonant discourses (inter-cluster dynamics); and, at the same time, create antagonistic frontiers between their alliance and other antagonistic ones. Carpentier uses a palm-tree model (below) to conceptualise such formations (Carpentier, 2017, p. 184).

Synthesis and Discussion 283

Figure 9-4: Carpentiers palm -tree model of antagonism and agonism To map the schematic shown in Figure 9-3 with Carpentier’s palm-tree model, each frond in the latter model can be conceptualised as one of the discourse communities involved in each network. At the scale of the frond itself, original tweets play the role of the leaves. The collection of the leaves (tweets) formed through original tweets and internal retweets, creates a frond. What connects the fronds in an agonistic space—forming the trunk of the tree—are, therefore, the retweets and @mentions between these discourses (fronds). On the other hand, the antagonism between the Other-enemies manifests itself in terms of negative engagement, non-reciprocity, and active passivity.

284 Synthesis and Discussion 9.4 Active Passivity

The logics of discursive resonance and dissonance (or equivalence and difference) were also observed in the active passivity of discourse communities in the construction of both agonistic and antagonistic spaces. The analysis of inter-cluster information flows in Chapter 7, and the qualitative investigation of discourses in Chapter 8, showed that although communities are generally aware of the presence of the Other-enemy in the Australian Twittersphere, they strategically make discursive choices about what/whom to engage with

(or not), and when. These decisions were also accompanied by strategic discursive choices about how different actors, issues, and discourses were framed and perspectivised. Communities of users in the different case studies were shown to intensify, mitigate or, at times, suspend their inherent antagonisms to achieve particular discursive goals.

In the #RoboDebt case study, for instance, it was shown that the Hard-Right discourse community, as the inimical antagonist of the Progressive cluster, actively retweeted the Other-enemy, thus forming a temporary networked discursive alliance with them against the vertical antagonist. However, such a case was not observed in the two other case studies; there, both sides of the antagonistic frontier actively avoided retweeting the Other-enemy. This avoidance mirrors the discursive strategies of mitigation and backgrounding identified by Reisigl and Wodak (2005). That is, the representation of an Us vs Them dichotomy is, at times, achieved through the negation of engagement with a discourse. This active passivity, or interaction through the very absence of interaction is, therefore, conceptualised as another form of articulatory practice in the dynamics of discursive struggle.

Synthesis and Discussion 285 Two primary observations are significantin this regard. First, the analyses in

Chapter 7 showed how active passivity can be rearticulated to further sediment one’s discourse against the Other-enemy. This was the case in the lack of retweets and/or @mentions across the antagonistic frontiers, such as the

Progressive discourse and its allies and the Hard-Right discourses. Given the extreme dissonance observed between the two discourses, I showed that unless there is a stronger need for interaction—such as in the case of the #RoboDebt study—there is generally no interaction between the two communities.

At the same time, I argued how the active passivity towards the discourse of the Other is also always contingent and dependent on the logics of the platform. The Hard-Right discourse community, for instance, cannot benefit as much from the practice of active passivity on the platform, simply due to its non-hegemonic position in the Australian Twittersphere. Since the users on the platform are generally more inclined towards left-leaning, progressive discourses (S. Park et al., 2018), the smaller community of the Hard-Right has to engage with the rest of the network in radically antagonistic ways if it wants to survive and maintain visibility. Under the logics ruling social media platforms—which require the users to amplify their visibility—this non- hegemonic discourse simply cannot remain silent. This is much in line with the process involved in the presence of a hardcore, vocal minority, as Noelle-

Neumann theorises (1974).

The same logics lead to the second observation with regard to active passivity.

I showed how the most hegemonic, larger community of users in the three case studies—the Progressive community—uses this active passivity to form an antagonistic frontier between themselves and the Other-enemy, by not giving them the visibility they require. At the same time, the Progressive discourse

286 Synthesis and Discussion community acts as the one unifying discourse in the equivalential chain to which others need to connect. The hegemonic position of this community, however, does not require them to reciprocate the formation of networked discursive alliances. In this sense, it is the non-hegemonic and smaller discourses that need to form the alliance with the Progressive discourse, but not necessarily the other way around.

Active passivity, in this sense, is itself an articulatory practice which can be employed to create agonistic or antagonistic spaces. However, the darker side of this articulatory practice is its inherent potential to create antagonistic spaces that can deprive one of engaging with the discourse of the Other. If, as

Mouffe theorises, “the task of democracy is to transform antagonism into agonism” (Mouffe, 2005, p. 24), and if the conflicting parties are to acknowledge the legitimacy of their opponents without resorting to violence, some level of interaction and engagement is the necessary condition of agonism. Passivity eliminates such potentials, and itself becomes a form of discursive violence.

What the active passivity of the hegemonic discourse in the Australian

Twittersphere can lead to, in effect, isa reproduction and amplification of discursive positions through the negation of interaction with dissent and disagreement, whether internal or external. This, in turn, can create a “spiral of silence” (Noelle-Neumann, 1974) within a community, where silence towards the Other becomes the expected norm of practice. However, such a practice is in direct contrast with the necessary requirements of the transformation of antagonism to agonism. While, on the surface, active passivity can manifest as a form of consensus among the collective of progressive users, it creates this consensus not through interaction, but through silence and elimination of the

Other’s voice.

Synthesis and Discussion 287 This could create two inherent issues. First, it can lead to further radicalisation of the Other-enemy. As Noelle-Neumann (1974) shows, an already- marginalised minority that has nothing to lose is much more likely to voice counter-hegemonic opinions and rely on sources and actors that confirm its opinions. This can further reinforce their views, hence radicalising them even more, and eliminating any likelihood of their being willing to change their opinions. Second, active passivity can create a space built on excessive forms of identity politics, in which conformity to the norms and behaviours of the collective becomes more important than the political progress itself (Section

3.4.2). In such a space, the aim of interactions will not be to reach a democracy built on agonism, but for the participants to “outdo each other in displaying their commitment to the community’s shared beliefs” (Bruns, 2019, p. 52). In this sense, the spiral of silence feeds into “a spiral of ideological reinforcements”

(ibid.). Within this space, the amplification of identity and radicalisation of beliefs and opinions will eventually lead to a deeply polarised antagonistic frontier, with severe repercussions for interaction, conversations (O’Hara,

2014), and agonistic goals.

This brings the discussion to another pressing question that has been a matter of debate in media and communication studies for some time now: Do filter bubbles and echo chambers exist and, if so, why? Given the polarisation tendencies observed in this project, the question is to what extent this polarisation is influenced by the technological infrastructure of the platform, and what impact it can have on a democratic project.

9.5 Filter Bubbles and/or Echo Chambers

The questions of filter bubbles (Pariser,2011) and echo chambers (Sunstein,

2007), their presence (or lack thereof), the dynamics involved in their creation,

288 Synthesis and Discussion and the role of platforms in their formation, have been matters of interest in recent discussions of social media platforms. Given that filter bubbles and/or echo chambers can be detrimental to a democratic project (Ksiazek, Malthouse,

& Webster, 2010; Sunstein, 2007), it is necessary for this research to also segue into this discussion, with reference to its three case studies. The question I focus on in this section, therefore, is whether any of the observed patterns in this study can point to the presence (or absence) of filter bubbles and/or echo chambers in the Australian Twittersphere.

Although the terms ‘filter bubble’ and‘echo chamber’ are sometimes used interchangeably (Bruns, 2017), it is worth examining the two concepts as referring to two different phenomena. The notion of filter bubbles, as the name suggests, is generally a reflection on the flow of information on social media platforms. We can consider a space as constituting a filter bubble when information from outside the space does not enter the bubble, likely because of the technological affordances of a platform, such as the personalisation of news and the algorithmic filtration of information (Beam, Hutchens, & Hmielowski,

2018).

An ‘echo chamber’, again reflecting its label, constitutes a space in which the internal discourse of the members is echoed permanently, without leaving the chamber (Dubois & Blank, 2018). In other words, in the discussions of filter bubbles and echo chambers, the directionality of information flows is always a matter of significance. From another perspective, the discussions of filter bubbles and/or echo chambers can be conceptualised in terms of what leads to their creation. Bruns (2017) and Bradshaw (2016), for instance, emphasise the agency and choices of users in the formation of filter bubbles and echo

Synthesis and Discussion 289 chambers. Bruns (2017) defines the twoterms in the following way (emphases in the original):

An echo chamber comes into being where a group of participants choose to preferentially connect with each other, to the exclusion of outsiders.

A filter bubble emerges when a group of participants, independent of the underlying network structures of their connections with others, choose to preferentially communicate with each other, to the exclusion of outsiders.

Treating the two concepts as two different phenomena, Bruns (2019) argues that it is possible for a communication space to be one, but not the other, or both at the same time. For instance, a tight-knit community of users who only follow each other on Twitter can constitute an echo chamber, in which a tweet by one user is visible to all their followers, but nobody on the outside. However, this space is not necessarily a filter bubble, since users can still share information from outside the chamber; for example, by sharing URLs to outside sources or @mentioning other Twitter users. Additionally, members in this echo chamber can still follow a hashtag and be exposed to tweets by others who have also used this hashtag, even if they are not in the list of the user’s followers.

The opposite can also be true. A group of users with a diverse number of follower connections might choose to only interact (e.g. retweet or @mention) with each other, but not with the rest of their followers or with tweets that they are exposed to. In such a case, this group of users is not necessarily in a sealed echo chamber; however, their communicative choices position them in a filter bubble. Where users only follow, and communicate with each other, they can be argued to be both in a filter bubble and an echo chamber (Bruns, 2019, pp. 15–38).

290 Synthesis and Discussion Given the brief discussion above, it is evident that the discussions around these concepts mainly focus on four primary factors in the formation of filter bubbles and echo chambers: platforms and their algorithms; users and their agency; the tendencies of people to form like-minded communities; and the existing political structures in societies (see, for instance, the review of literature by Tucker et al., 2018). Regardless of definitional differences, the main question for all scholars investigating these concepts is whether or not users are exposed to information that does not confirm theirdiscourses and, if they are not, why not? In the following discussion, I focus on these two questions, by 1) discussing the presence or absence of hermetically sealed spaces that do not allow non- confirming information to enter the communication space (echo chambers); and

2) by determining whether interactions occur in spaces where confirming discourses are amplified, and non-conforming ones are silenced (filter bubbles).

The investigation of the URLs shared by the different communities in each case study (as discussed in Chapter 6) showed that information sources such as mainstream media outlets, prominent journalists, and different organisations are widely shared by all the discourse communities involved in the studies.

This is even the case in the more polarised debates over Section 18C of the

RDA in Australia, and can be considered as evidence of an absence of hermetically sealed spaces. That is, even the most extreme poles in the political spectrum are exposed to factual, well-established, and sometimes non- confirming views. Of course, in line with findings in similar studies (e.g. Bakshy et al., 2015; Garrett, 2009; Spohr, 2017), it was also shown that the more partisan news and information sources are shared by the extreme poles to further reinforce their discourses. In this sense, we can come to the conclusion that non-confirming information does enter the discursive spaces of Twitter users, even if they do not agree with it. However, they choose not to further

Synthesis and Discussion 291 disseminate such information, and to rely on ideologically confirming information sources instead (e.g. Buzzfeed and Junkee for the Progressive discourse, and Voice of Europe and Breitbart for the Hard-Right). These findings, therefore, show that the widely shared partisan views in the form of

URLs are not simply due to the algorithmic curation of information on Twitter.

On the contrary, although users are exposed to non-confirming information, they choose to supplement such information with information from more partisan sources.

To further scrutinise this issue, I turn to the investigation of inter-cluster information flows (as discussed in Chapter 7). By comparing the amount of information flowing within and among different discourse communities— especially in the form of retweets and @mentions—I showed how there is always a free flow of information in the discursive environment, even between the inimical antagonists. This further supports the finding that a hermetically sealed space does not exist in the communication environment. Even in the most polarised case study (Section 18C), analyses (in Chapter 7) showed that there is a high level of retweeting—and especially @mentioning—between the antagonists. Therefore, no evidence of the presence of echo chambers as hermetically sealed spaces was found in the three case studies. In reference to

Bruns’ (2017, 2019) definitions of the terms (given above), ‘connections’ are definitely there; however, with regard to the question of ‘communications’, the concept of filter bubbles poses more complexities.

An important factor in the consideration of the concept of filter bubbles is the boundaries and limits of a space to be considered as ‘a bubble’. In other words, the question is about the standards or thresholds against which we can compare the information flows, so as to come to a conclusion about whether or not a

292 Synthesis and Discussion filter bubble exists. For instance, in the case of the Immigration case study, I showed how each community reproduces its own discourses—through original tweets—to maintain their identification with the resonant discourse. If we set the boundary at this level, it can lead us to argue that each community is, in fact, operating within a filter bubble. However, I also showed that within the equivalential chains created through the affordances of Twitter, communities construct networked discursive alliances. In these alliances, they amplify the discourse of the Other-adversary and form a larger agonistic cluster in the hegemonic project (mainly through retweets and @mentions). Again, if we set the boundaries at this scale, we might be able to argue that there is a large filter bubble in which a set of discourses is amplified.

Even in this case, however, a conclusion with regard to the presence of filter bubbles and/or echo chambers is quite hasty and unwarranted. There are two key issues in this regard. The first issue is that the identification of a particular communication space as either a filter bubble or an echo chamber is not necessarily a binary choice (Bruns, 2019). The question regarding such spaces is not an either/or question, but rather a question of the degree to which a space resembles an echo chamber or a filter bubble. The answer to such a question partly relies on setting a standard against which one can draw conclusions regarding the degree to which a given community is in a filter bubble or an echo chamber.

This raises the second issue. Even if we set the boundaries at the scale of the whole platform, and observe communication spaces that can be considered as filter bubbles or echo chambers, the question then is whether participants in this platform only receive their information from this platform. As Dubois and

Blank show (2018), this is far from reality. The majority of users on social

Synthesis and Discussion 293 media platforms have a rich and diverse ‘media diet’ and, even if they are not exposed to non-conforming information on one platform, they are exposed to it elsewhere. Furthermore, even if communication spaces comprise filter bubbles and/or echo chambers, the two key factors to consider are 1) that their effects on polarisation are generally small; and 2) that we need to consider whether these spaces are due to ‘pre-selection’ of information by the platform, or ‘self-selection’ by users (Zuiderveen Borgesius et al., 2016). Therefore, when considering the polarisation observed in this project, it seems a hasty generalisation to simply blame echo chambers/filter bubbles and the platform as the primary cause of polarisation.

This is where the concept of active passivity and its implications become even more significant. The discussions around the concept of filter bubbles often revolve around the line of argument that the presence of such spaces on social media platforms is detrimental to the democratic project, since it deprives users of exposure to the discourses with which they are in disagreement, thus further increasing the risk of radicalisation of discourses and polarisation of societies

(Evolvi, 2017; Ksiazek et al., 2010; Sunstein, 2007). In referring to such detrimental factors—such as radicalisation of discourses, dissemination of fake news, right-wing and white supremacist discourses (Zannettou et al., 2018), or anti-vaccination beliefs (English, 2017)—the generally unspoken discursive position is an antagonistic position against the possibility of an amplification of non-hegemonic voices.

As the analyses in this project showed, however, it is in fact the non-hegemonic discourses which engage more with their antagonistic discourses. Upon being exposed to the discourse of their antagonist, the hegemonic discourses do not necessarily attempt to engage or interact with it. In other words, mere exposure

294 Synthesis and Discussion to the discourse of the Other cannot guarantee a diversity of opinions, civil deliberative discussions, and healthier democratic projects; rather, as some argue, such exposure might even have the opposite effect (Bail et al., 2018).

The hegemonic antagonists in this study operate within the logics of difference and a ‘no platforming’ strategy to silence the Other-enemy. This, in turn, might lead to even louder voices on the part of the enemy because of the logics of the platform, which mandate activity and visibility in its media ecosystem. The active passivity on the part of the hegemonic discourse (Progressive Politics in the case studies of this project), in effect therefore, creates a space in which their voice is echoed within the broader limits of the discursive formation. On the other hand, the community that we often fear to be in a filter bubble—the

Hard-Right discourse—is in fact the one most actively interacting with the discourse of its Other-enemy. The active passivity on the part of the hegemonic discourse of progressive politics, therefore, could potentially deepen antagonistic frontiers rather than create agonistic spaces.

Considering the above points, the answer to the question of filter bubbles depends on how we define and operationalise the notion. Setting the boundaries too tightly, we might come to the hasty conclusion that there are, in fact, filter bubbles involved in the case studies. However, the fact that cannot be ignored here is the role of discourses and agency in the creation of such spaces. It is not the platform that creates them; rather, they are created by the users operating on the platform, and by their articulatory logics and discursive strategies. Of course, at the same time, the users on the platform construct these filter bubbles using the affordances of the platform itself. However, as discussed throughout this thesis, the re-articulation of contingent affordances of the platform is always in a knotted fashion alongside the discursive and agentic aspects. Blaming the platform for the creation of filter bubbles,

Synthesis and Discussion 295 therefore, is a technologically deterministic and essentialist argument that overlooks the complexities of discursive struggle and polarisation.

What then, if we do not set the boundaries at the level of communities on a platform—or, as Dubois and Blank argue (2018), on a single platform alone— and take a bird’s eye view of the whole communication environment instead?

In such a case, I argue that discourse theory alone, without the empirical evidence provided in this study, can shed light on this question. Reflecting on the concept of radical negativity (ineradicability of antagonism), as theorised by Laclau and Mouffe (2001), we can argue that since the very possibility of the existence of discourses and identities depends on an awareness of the existence of the Other(s), filter bubbles and echo chambers, in the way they have been formulated in the media and some scholarly literature, are theoretical impossibilities.

It is only through the radically negative differential position against the Other that a signifier, a moment, or a discoursefinds its meaning. Therefore, even if this differential position is not acknowledged, and no interaction is observed between the discourses, the polarisation between them is the result of this radical negativity, and not of the technological structures of the platform. As

Bruns (2019) argues, in many cases, partisan platform users deliberately follow non-confirming information in order to be aware of the positions of the inimical antagonist; to better equip themselves to address the discourse of the Other; and to “reinforce their sense of the superiority of their own perspectives”

(Bruns, 2019, p. 82).

Within media and communication studies, the manifestation of this radical negativity is often referred to as ‘homophily’ and ‘selective exposure’ (Garrett,

2009; Kossinets & Watts, 2009; Macskassy & Michelson, 2011; McPherson et

296 Synthesis and Discussion al., 2001; Messing & Westwood, 2014; Spohr, 2017). However, homophily alone cannot satisfactorily explain polarisation, filter bubbles, and echo chambers

(Bruns, 2019; Zuiderveen Borgesius et al., 2016). Similarly, anti-homophily and de-polarisation measures are not feasible solutions (O’Hara, 2014). I would go as far as arguing that even if platforms force the antagonistic discourse onto a community—as has been the case in new developments on YouTube (Wong &

Levin, 2019) and Twitter (Romm & Dwoskin, 2018)—such polarised spaces will still occur, simply due to individuals’ avoidance of the discursively dissonant. In such a case, the articulatory practice of active passivity finds its place once again. Even if the discourse of the Other-enemy is forcefully injected into the discourse of a community, it will either be silenced and ignored through active passivity, or discursively attacked to ensure its elimination. Exposure alone in no way guarantees civil deliberation.

The question to focus on, therefore, is not whether filter bubbles and/or echo chambers exist or not. While an ideal solution might be to simply put these notions aside completely, it seems that they are here to stay (Bruns, 2019, p.

33). Such discussions divert us from focusing on the more important issues.

What was observed here, and perhaps in many other studies of social media platforms, is much deeper than a question of media effects. As Bruns observes, what we are witnessing is “polarisation, not fragmentation” (Bruns, 2019, p.

105). And this polarisation is not the effect of some technological design. On the contrary, it is the amplificationof antagonism, and the avoidance of communication by platform users.

A key focus of a democratic project on a social media platform should be on ways to avoid the active passivity generally practiced by hegemonic discourses, and on ways to form agonistic discursive alliances with the Other-enemy, thus

Synthesis and Discussion 297 transforming them into an Other-adversary. Such a focus, however, goes far beyond the scope and limits of this project, and remains a topic for future research.

298 Synthesis and Discussion 10 Conclusion

This research examined the dynamics of discursive struggles in the Australian

Twittersphere. The findings presented inthis thesis contribute to our understanding of the inter-relationship between social media and democracy through the theorisation of the concepts of ‘networked discursive alliances’ and

‘active passivity’. This study showed how these two discursive practices lead to the construction of both antagonistic and agonistic spaces in the Australian

Twittersphere.

In the present chapter, I provide a summative account of the preceding chapters of this thesis. I then move to consider some of the solutions currently being proposed and pursued by social media platforms to address the increasing polarisation on, and beyond platforms. I also engage with the findings of this study to discuss whether they can help in achieving more agonistic social media. Finally, I conclude this study by outlining the future research directions that are required in order to gain a deeper understanding of the role that social media play in democracy.

10.1 Thesis Summary

The first five chapters of this thesis detailed the background of the study, its theoretical framework and sensitising concepts, and the methodological procedure used in its three case studies. The introductory chapter outlined the background of the study and its objectives. I then introduced the theoretical framework informing the project in Chapter 2. Specifically, this chapter detailed the project’s epistemological and ontological foundations, and introduced the theoretical vocabulary used in my interpretation and discussion of the findings.

Conclusion 299 In Chapter 3, I started with a review of the academic literature that investigates the inter-relationship of social media and democracy. Starting with views located at the extremes of cyber-utopianism and cyber-scepticism, I followed their line of thought and moved to theories and concepts that assume a more complex and less essentialist relationship between social media and democracy. Synthesising these views and re-reading them from a discourse- theoretical perspective, this chapter showed how the dominant mode of thought concerning the democratic potentials of social media platforms has generally relied on liberal and deliberative models of democracy.

Chapter 3 then continued with a review of literature that engaged with other complexities involved in understanding the communication spaces provided by social media platforms; namely, their materialities, horizontal and vertical contexts, and the possible role that their design and technological structures might play in the formation of polarised spaces. Finally, I focused on Twitter as an object of study, and engaged with the particular cross-section of Twitter that formed the foundation of the case studies in this project—that is, the

Australian Twittersphere.

Chapter 4 set out the methodological process and design of the study, and provided the rationale for a mixed-methods design. In the design of the methodology, a key concern for me was to adopt a methodological pipeline that could operationalise discourse theory; that could account for large-scale datasets; that would provide an opportunity for deeper qualitative insights; and, more importantly, that would allow for a recursive move between the different stages of analysis.

After introducing the methodological design of the project in Chapter 4, and the context and history of the three case studies in Chapter 5, the next three

300 Conclusion chapters each focused on specific aspects of the communication patterns and dynamics of discursive struggles.

In Chapter 6, I focused on collective and aggregate metrics and communication patterns, mainly drawing from social media analytics. The focal points of this chapter were the intra- and extra- cluster dynamics of discursive struggles. The chapter showed how different clusters in the Australian Twittersphere reproduce and amplify their discourse by sharing URLs to discursively resonant information sources. Furthermore, the examination of secondary hashtags in each case study showed that although the different clusters and discourses contribute to the conversations by using the primary hashtags and keywords used by the majority of users, users supplement the primary hashtags with other hashtags that mark the particularities of their discourse. The findings of this chapter also showed how hashtags are strategically used by different communities to recontextualise differenttopics and to expand the limits of the discursive formations in which a community operates.

Chapter 6 also investigated the dynamics of visibility and opinion-leadership.

The analysis in that chapter showed how the affordances of Twitter are strategically discursified to give morevisibility to the opinion leaders with discursively resonant views. In this regard, I showed how retweets are employed to amplify the voice of “crowd-sourced elites” (Papacharissi, 2015) and prominent social actors in each cluster. My close reading of the Twitter profiles of the most retweeted accounts by each community of users showed that the accounts receiving the highest number of retweets from a collective of users are generally those that share similar discourses. On the contrary, communities discursified the affordance of @mentioning to engage with both the discursively resonant and dissonant actors.

Conclusion 301 Chapter 7 focused on the different network formations created as the result of the particular ways in which collectives of users discursify the affordances of

Twitter. In this chapter, I examined the various networks formed through retweets and @mentions, in order to investigate the dynamics involved in the amplification and reproduction of discourses (intra-cluster dynamics); the discursive struggles between antagonistic clusters (inter-cluster dynamics); the flow of information within, between, and among clusters in the Australian

Twittersphere; and the information flows among these clusters and accounts beyond the Australian Twittersphere (extra-cluster dynamics).

The analyses provided in that chapter led to the theorisation of the concept of

‘networked discursive alliances’. I showed how users strategically form discursive alliances with other communities in order to amplify their voice, gain discursive power, and further hegemonise their discourse. My investigation in this chapter further examined how users discursify the affordance of retweeting to construct these networked discursive alliances in a way that enables them to construct a chain of equivalence while at the same time, maintaining their particular identifications. In the context of the Australian Twittersphere, I showed how the discourse of progressive politics often plays the role of the unifying discourse with which other discourses identify and form the equivalential chains against a common antagonist.

With regard to the common antagonist, a key finding in this project is the contingency involved in the discursive construction of an inimical antagonist.

While in political discussions such as the 18C and Immigration case studies, the antagonistic frontier was constructed between the Hard-Right and

Progressive discourses, this was not the case in #RoboDebt. In this particular case, the antagonistic discourses of Hard-Right and Progressive Politics formed

302 Conclusion a networked discursive alliance against a common enemy positioned in the vertical context. This particular finding shows the potentials for, and the possibility of, the construction of agonistic spaces in the Australian

Twittersphere.

In synthesising the findings in that chapter (7), and by examining the information flows between the different clusters, I also showed that the polarisation observed in the 18C and Immigration case studies is not simply due to the technological design of Twitter. Rather, it is due to strategic discursive decisions made by the users involved in the conversations. Through particular discursifications of the material affordances of the platform—such as retweeting discursively resonant actors—Twitter users formed networked discursive alliances. At the same time, through the negative articulation of antagonisms, they strategically refrained from retweeting (i.e. amplifying) the discourse of the inimical antagonist. This led me to the concept of ‘active passivity’.

What active passivity implies, therefore, is that the absence of interaction, and the presence of polarisation, is not simply due to filter bubbles and/or echo chambers. Additionally, polarisation does not simply occur as the result of a process of “selective avoidance” (Weeks, Ksiazek, & Holbert, 2016), where users make choices so that they are not exposed to the discourse of the Other-enemy.

As the findings in Chapters 6 and 7 show, clusters are, in fact, constantly exposed to the antagonistic discourse through URLs, hashtags, and @mentions.

However, they make the active decision to remain impassive. The fine line I draw between selective avoidance and active passivity, in this regard, is that to me, avoidance implies a sense of not being exposed to something. ‘Active passivity’, however, implies the ‘action’ of remaining impassive and not

Conclusion 303 reacting, despite being exposed to something. In other words, my understanding of the word ‘avoidance’ implies an absence of articulation, while

‘active passivity’ implies articulation through the negation of interaction.

Finally, in Chapter 8, I turned my attention to the particular ways in which users maintain their identification withthe most resonant discourse, while at the same time forming networked discursive alliances with their adversarial antagonists. The primary issue in this chapter was the intra-cluster dynamics involved in the reproduction of discourses. Analysis of keywords in the discourse of each community showed how users reproduce their subject positions by framing issues from their own discursive perspectives. My close reading of original tweets posted by each cluster showed that although the different clusters in a networked discursive alliance focus on the same topics, and generally discuss issues to achieve the same end-goals, they do so by using discursive strategies and frames particular to their own discourse. In this way, each cluster is able to maintain its particular identification while joining the more hegemonic cluster in the equivalential chain.

On considering the findings of that chapter in the light of previous chapters, I showed how users in the Australian Twittersphere discursify retweets to form networked discursive alliances (inter-cluster dynamics), while using original tweets to maintain their identification with the communities to which they belong (intra-cluster dynamics), and to disseminate their discourse to the broader communication environment (extra-cluster dynamics).

Chapter 9 further synthesised the findings of the study, and expanded the concepts of networked discursive alliances and active passivity. I then used these concepts, and the findings of the study, to also engage with the recent debates over filter bubbles and echo chambers, their presence (or lack thereof)

304 Conclusion in the Australian Twittersphere, and the possible causes of communication spaces that might resemble filter bubbles and/or echo chambers. In this regard, the findings of this study, and their interpretation from a discourse-theoretical perspective, showed that filter bubbles and echo chambers were not present in the three case studies.

Furthermore, from a discursive-theoretical perspective, I argued that these concepts are, in fact, theoretical impossibilities, since the very presence of political identity is predicated on an awareness of the antagonistic discourse.

In this sense, even in the most polarised case study of the project—Section

18C—each discourse was not only generally aware of the presence, discourses, and arguments of the Other-enemy, but was also exposed to them. However, participants strategically chose not to engage with their inimical antagonist— especially if they perceived themselves to be in the hegemonic discourse.

10.2 Polarisation, Its Solutions, and Their Implications

Although the present research was a case-based study, and I investigated the dynamics of discursive struggles in three particular socio-political cases in the

Australian Twittersphere, the findings of this research raise important considerations regarding polarisation on, and beyond, social media platforms; and our current understanding of the role of social media in democracy. In this section, I reflect on the particular findings of the case studies, and discuss their implications for the broader question of the inter-relationship of social media and democracy.

The three case studies examined in this project showed that both antagonistic and agonistic spaces form in the Australian Twittersphere, and that the

Conclusion 305 technological design and affordances of Twitter do not necessarily play a major role in the formation of such spaces. Technologically deterministic views often blame the platform’s (algorithmic) design for polarisation. On the contrary, however, temporary agonistic opportunities can manifest, and otherwise- disconnected antagonistic discourses can construct networked discursive alliances, precisely as the result of the platform’s affordances. The affordances of the platform, and the way it has been designed, do not play the main and defining role in dictating how information flows between discursive-material clusters; rather, it is the strategic engagement of users with these affordances, and their particular discursification of them, that play the most significant role.

Although agonistic formations do occur organically, as was the case in the

#RoboDebt case study, it is worth noting that this particular case was not a topic directly in the domain of politics. In the other two case studies, which were more directly interwoven with politics, a strong antagonism between the

Hard-Right and Progressive clusters was the defining feature of the communication space. In these two cases, agonistic formations occurred across the antagonistic frontiers.

With regard to the inter-relationship of social media and democracy, the two key findings of this research—networked discursive alliances and active passivity—are particularly important. In the following paragraphs, I engage with these findings. I first explore some of the recent discussions of the roles and responsibilities of social media platforms in democracies; and the legal, economic, and political pressures put on platforms to ensure better communication spaces.

At the level of social media platforms and what they can or should do to ensure a better communicative space regarding their role in democracy, recent debates

306 Conclusion mainly focus on the implementation of processes and procedures aimed at the elimination of undesirable content, discourses, and actors from social media platforms. The founders and CEOs of platforms have been asked to answer questions and provide testimonies in political settings, such as the European

Parliament (Hern, 2018) and the United States Senate (“Transcript of Mark

Zuckerberg’s Senate hearing,” 2018), particularly regarding their roles and responsibilities during elections. They have also started conversations with political figures to implement better practices, especially with regard to extremism (see, for instance, Kayali, 2019).

In another example, Twitter introduced an initiative to “increase the collective health, openness, and civility of dialogue” on its platform (Twitter, 2018).

Project proposals that were accepted aimed to measure the health of conversations on the platform, and to introduce measures to bridge the gaps between communities on Twitter, so that they are exposed to information from diverse perspectives (ibid.). Similarly, Facebook’s CEO, Mark Zuckerberg, stated in a blog post that “we have a responsibility to keep people safe on our services”, and that Facebook has focused on building better-trained, more complex artificial intelligence (AI) algorithms to “proactively report potentially problematic content to our team of reviewers, and in some cases to take action on the content automatically as well” (Zuckerberg, 2018).

More recently, Facebook also banned a number of extremist users from its platforms (Wagner, 2019). Some evidence suggests that YouTube has also recently changed its algorithms so that alt-right channels do not appear in its list of recommended videos (Suzor, 2019). In the wake of the 2019 terrorist attack in Christchurch, New Zealand, the Australian government introduced a bill to jail and/or fine social media executives if they do not remove violent

Conclusion 307 material quickly enough (Conifer, 2019). Facebook also introduced new changes to its livestream rules following the attack, stating that people who violate the platform’s “most serious policies” will be banned from the platform for a period of time (Kelly, 2019).

Apart from the fact that a focus on content removal is generally an after-the- fact solution, and could only work once such content is posted, a deeper look at the proposed solutions reveals two of the issues highlighted in Chapter 3: a reliance on technological solutionism, and the underlying liberal democratic thinking in our current understanding of social media platforms. Simply put, the abovementioned solutions propose that through the use of better technological design and/or better regulation of content, we can reach a communication space in which consensus can form.

Solutions aimed at the removal of undesirable content, discourses, and actors very often draw from medical or criminal justice metaphors, and promise users a ‘healthy’ or ‘safe’ space where they can participate in conversations and share their opinions. By relying on such metaphors, this mode of argumentation posits that anything that poses problems in the communication space should be viewed as a disease or a danger. If left unaddressed, a disease or danger can spread to the whole environment and corrupt it. The logical and easy solution, therefore, would be to remove it. It is worth entertaining this health metaphor more deeply, and considering whether the proposed solutions to the problems facing social media platforms can achieve the desirable outcomes they seek.

As the discussions in Chapters 2 and 3 show, any form of consensus is primarily built on hegemony and exclusion. The ideal ‘healthy’ communication space sought by social media platforms can be conceptualised as a space built on a form of consensus, in which all users can feel they have a voice and the freedom

308 Conclusion to express that voice. Extremist users and discourses, however, threaten such a space. The removal of problematic users, content, and discourses from the platform, therefore, is the obvious and logical conclusion. Even at their current state of highly criticised, non-transparent, and inconsistent moderation practices (Gillespie, 2017), mainstream social media platforms generally have clear community guidelines with regard to what they perceive as problematic,

(e.g. threats of physical violence, racial and sexual discrimination, and various levels of pornography).

Building on the medical/legal metaphors, therefore, any user, content, or discourse that violates the guidelines of the platform—that is, the ‘health’ and

‘safety’ of others or their conversations—will be removed. On the surface, this approach seems to be completely sensible and practical. However, a number of issues arise when considering the complexities involved in this regard. First, there is the issue of the solutions themselves. Even if we assume that the platforms will be able to successfully implement these solutions through the use of technological measures—such as better AI, more human content moderators, or consistent and transparent moderation practices—the achieved outcome would, at best, be at the level of symptomatic treatment (to use similar medical metaphors). The problem with the symptomatic treatment of a problem, however, is that it does not necessarily address the source of the problem and its underlying causes. In the platform context, it merely focuses on the elimination of texts, but not the discourses. At times, eliminating the symptoms of a problem can be even more dangerous, as the disease itself is then masked.

At the risk of stepping into (personally) unknown territory, symptomatic treatment is generally the preferred route for medical professionals when the

Conclusion 309 body is able to eliminate the disease on its own; when there is no cure for the disease; or in the case of palliative care (see, for instance, Darmansjah &

Hagqvist, 2008). I do not believe that any of these scenarios is an apt analogy for the problems facing communication spaces on social media platforms.

Social media platforms are not isolated communication spaces that operate completely separately from each other, the broader media environment, or society (Dubois & Blank, 2018). Therefore, solutions aimed at elimination generally pose two problems. First, a user banned from a platform can easily move to another part of the broader online media ecosystem, such as alternative platforms and online bulletin boards (e.g. Gab, 4chan, or 8chan).

Second, eliminatory solutions assume a fixed mode of practice for problematic discourses. In other words, they overlook the creativity and agency of users in finding new and more complicated ways to ‘game the system’ (Gerrard, 2018;

Milner, 2013); for example, through the use of coded language, emoji with community-specific meanings (Nagesh, 2017), and gaslighting (Jack, 2017).

These problems become even more pronounced when considering the performativity of behaviours online. Social media platforms do not have reliable ways of inferring why certain users and communities behave in certain ways.

This makes moderation of their content increasingly difficult, since performative modes of communication (Papacharissi, 2012) can always find new ways to mask themselves and their intentions. Such solutions create a never-ending battle and a constantly shifting battleground between the platforms and users who promote troubling discourses. The continuation of such a battle, in effect, diverts our attention from the more systemic root causes of problems. This brings me to another issue with regard to the currently proposed solutions. Moving away from the discussion above, which focused

310 Conclusion solely on the solutions, the next complexity involved with moderation practices concerns the notion of consensus, and the hegemonic practices involved in the formation of the consensus sought by the platforms.

As discussed in Section 3.4, mainstream social media platforms primarily operate through a capitalist logic, where maximisation of profit is the ultimate goal. Therefore, such platforms are much more likely to adopt practices that ensure higher profits. Up to a certain point, platforms used to benefit from the presence of controversial actors and content, since these increase user engagement, and ensure more money-making opportunities. However, with increasingly more corporate partners adopting explicit political positions and pressuring platforms to remove their ads from spaces associated with controversial actors, along with the socio-political pressure on platforms to address problematic discourses, platforms have had to adapt by becoming more in tune with the hegemonic discourses.

On the surface, the outcome of such a situation might seem desirable: If more corporate partners distance themselves from problematic discourses and pressure platforms to do likewise, it might lead to the proliferation of more desirable (and perhaps more progressive) content on the platforms, given the hegemonic position of progressive discourses on platforms. However, the flip side of this process, or the hegemony of capitalism, might not be so desirable:

It is this very hegemony that creates many of the root causes of the problems, such as inequality and polarisation, in the first place. In following neoliberal and capitalist logics and adapting to their pressures and demands, platforms will continue to reproduce the same capitalist hegemony, and with it, the same root causes of the problems. That is, in the feedback loop created between the corporate partners and social media platforms, the primary goal will still be

Conclusion 311 the maximisation of profit for both the platform and the corporate partner. In effect, this process would virtually ensure the continuation of capitalist logics and further exploitation of users and their data, but this time, with a more progressive face. Briefly put, as long as the primary goal is the accumulation and maximisation of profit, and not democracy itself, the end-result will be the continuation of capitalist logics, and with them, continuation of the root causes of the problems.

10.3 Future Directions

The goals, scope, and limits of this project do not allow for a more in-depth discussion of capitalism and its inherent problems. Therefore, I do not further the above discussion at this point; however, this discussion does lead to the two key findings of mystudy: networked discursive alliances and active passivity.

As discussed in Section 7.5.3, the more hegemonic discourse in the Australian

Twittersphere is the discourse of progressive politics. My analysis in Chapter

7 showed how other clusters form networked discursive alliances with this discourse, and create chains of equivalence against a common inimical antagonist; that is, the Hard-Right. In parallel, this research also showed the dynamics of active passivity, and the way in which hegemonic discourses generally refrain from engaging with their antagonist through remaining actively passive towards them. In reflecting on these two dynamics, the question that remains worthy of further investigation relates to the likely consequences of these dynamics for democratic projects, especially in light of the proposed solutions discussed in the previous section.

312 Conclusion One possible outcome of the solutions proposed by platforms and legislators so far would be that the elimination of undesirable content and actors from platforms would indeed make them more deliberative, democratic, and agonistic. That is, through removing highly partisan, extremist, and violence- inciting users and content, platforms might be able to make users feel safer; this, in turn, might make them more likely to participate and engage in productive discursive struggles, and to form a communication space that is built on agonistic pluralism.

Another possible scenario is also likely, however. It is possible that even after undesirable content and actors are removed from a platform, the hegemonic discourses will continue their practice of active passivity towards their antagonists. That is, the progressive discourse might still remain highly passive towards the less hegemonic conservative voices in the Australian

Twittersphere. What such a situation could lead to, in effect, is a new sense of marginalisation (justified or not) for more conservative users, who might then be more likely to be attracted to the communication spaces where more discursively resonant actors are present (e.g. Gab, 4chan, or 8chan).

As discussed in Section 9.4, active passivity potentially reinforces spirals of silence (Noelle-Neumann, 1974) through the deepening of antagonistic frontiers. This creates a vicious cycle of polarisation and radicalisation, with potentially negative impacts on democracy. In such a setting, the amplification of antagonism and identity would be so deepened that no agonism would be possible. As the quote at the beginning of Chapter 9 indicates, it would effectively lead to a discursive war, in which the goal would simply be to continue fighting, with no regard for what started the war in the first place.

Conclusion 313 The practical outcomes of such a spiral of antagonism in the civic life of people could be devastating. An extreme and highly pessimistic outcome would be a deepening of radicalisation to a scale where it would turn to extremism and physical violence. At a less extreme scale, polarisation and radicalisation would mean increasing support for those political representatives who resonate most with non-hegemonic clusters. This, in effect, will mean an even further rise to power for more extremist and authoritarian discourses and politics, such as far- right and far-left views.

The past decade has witnessed an increasing trend towards this second scenario, with a concerning rise of populist and right-wing discourses, and an increasing anti-Other sentiment globally. With the 2016 election of Donald

Trump in the United States, Brexit in the United Kingdom, the increasing support for right-wing politicians and parties in the European Union elections, and the election of Bolsonaro in Brazil, similarities between these discourses have become more difficult to ignore. The latest elections in Australia also gave rise to similar concerns and criticisms, with the results of the election once again favouring the more conservative and centre-right Coalition over the progressive and centre-left Labor party.

Of course, not all the responsibility and blame for these patterns can be put on the progressive and left-leaning discourses. However, it can, at least partly, be indicative of a flaw in tactics and strategies employed by progressive discourses. What started as a progressive move towards equality for marginalised groups, has gradually lost its premises: The underlying causes have been obfuscated, and the intensification of difference and antagonism has taken over. This over-emphasis on antagonism, and the negation of confrontation with anyone other than one’s ingroups, has given rise to an

314 Conclusion increasing active passivity both online and offline. In effect, this not only opens the space for louder counter-hegemonic voices such as the Hard-Right; it also inverts the priorities that started these movements in the first place.

With an increasing active passivity, comes an increasing sense of deepening antagonistic frontiers. The goal, in such a setting, is not to move towards agonism, but to emphasise antagonism. The outcome of such strategies, even if successful, simply “obfuscates the problem instead of trying to resolve it”

(Žižek, 2017). This rather bleak picture of the outcomes of active passivity calls for a revision of this discursive strategy, and a revisiting of the starting points of the progressive movement and its democratic and egalitarian objectives.

Given that active passivity can potentially defeat its own purpose, one can increasingly see the need for a return to strategies and tactics that can move more towards agonistic rather than antagonistic principles. One potential solution could be what Mouffe calls aleftist populism (Mouffe, 2018). That is, instead of an over-emphasis on excessive forms of identity politics—which could potentially emphasise differences—the progressive left should return to a radically inclusive strategy that links all identities in a chain of equivalence, without privileging one over others.

It is perhaps too soon to come to a definitive conclusion about the likely impacts of the proposed solutions by, and for social media platforms. These platforms are currently at an experimental phase, trying different approaches and monitoring their success rate. While the discussion above primarily focussed on removal strategies, platforms are also testing alternative modes of content moderation. YouTube’s decision to limit its recommendation of problematic content, rather than its total removal, for instance, is one such approach. More time, and more in-depth interdisciplinary and empirical

Conclusion 315 investigation of social media and democracy are needed before we can answer such questions more conclusively. The present study is simply the first step in a long-term research project to explore these issues. For future projects, I would like to engage more with other disciplines, and focus on each of the strands in the discursive-material knot.

Psychological studies, for instance, could shed more light on user agency, and the way users react to different stimuli, such as active passivity. Within this discipline, studies that focus on ostracism on the internet (e.g. Williams,

Cheung, & Choi, 2009), and ostracism on social media in particular (e.g.

Rudert, Greifeneder, & Williams, 2019; Schneider et al., 2017), could prove valuable. Sociological and philosophical debates could inform the consideration of discourse and structure, and studies from new materialist perspectives could further support our understanding of the role of platforms’ material affordances

(e.g. Carpentier, 2017, 2019; Marres, 2014, 2017).

This research has also indicated the presence and possibility of the formation of spirals of silence. Future research in this area could help us to better understand the dynamics involved in the creation of spirals of silence on political social media spaces. Additionally, studies on media effects could explore the processes involved in the formation of opinions, and the levels of exposure required to accept new discursive identifications. In this regard, studies that investigate selective and incidental exposure and its effect on users, could deepen our understanding. (See, for instance, Heiss & Matthes [2019] and

Lee, Shin, & Hong [2018] for political topics; and Orben & Przybylski [2019] for psychological effects of media on youth).

Another venue for future investigation is to examine the role of malicious coordinated efforts to foster polarisation and antagonism. Especially given the

316 Conclusion fact that, as shown, increasing polarisation could play a detrimental role in a democratic project, it is more than ever important to study the links between malicious activity in some clusters in the Australian Twittersphere and possibly internationally coordinated misinformation campaigns and projects.

Finally, the present research focused on a single platform, and a single socio- political context. Given the interwoven nature of social media platforms today, it is necessary to compare the findings of this study with research on other platforms, and in other geographical settings. Finally, the incorporation of other methodologies, such as surveys and interviews, could supplement our understanding of users’ online experiences and choices. Indeed, there are still many unanswered questions before we can conclusively understand the inter- relationships between social media and democracy. Nevertheless, one thing is certain for me at the moment: The root of the polarisation we are facing today is not technology, and solutions to this problem cannot be purely technological.

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