Online Boycott and Collective Action: User Goals, Behaviour and Platform Affordance

Online boycott and collective action: User goals, behaviour and platform affordance

Thesis submitted in partial fulﬁllment of the requirements for the degree of

Masters of Science in Exact Humanities by Research

Shantanu Prabhat 201456190 [email protected]

International Institute of Information Technology, Hyderabad Hyderabad - 500 032, India November 2020 Copyright c Shantanu Prabhat, 2020 All Rights Reserved International Institute of Information Technology Hyderabad, India

CERTIFICATE

It is certiﬁed that the work contained in this thesis, titled “Online boycott and collective action: User goals, behaviour and platform affordance ” by Shantanu Prabhat, has been carried out under my super- vision and is not submitted elsewhere for a degree.

Date Adviser: Prof. Nimmi Rangaswamy To my lovely family Acknowledgments

This work wouldn’t have been possible without the unﬂinching support and guidance of my advisor Prof.Nimmi Rangaswamy, who has always been ready to listen to my half-baked thoughts and transform them into ideas of substance. Beyond this thesis, she has been instrumental in helping me get a foot in the door of HCI and UX and turn my interests in this space into a viable career choice. I immensely value her pushing us beyond the standards that we set for ourselves. I will always cherish our discussions on everything under the sun, and listening to her positive perspective on many of those issues. I count myself very lucky to work under an immensely student friendly professor who often challenged traditional norms of our academic structure in college.

I am really grateful to my friends, Dhruv, Isha, Shreedhar, Sneha for being my rock solid support and being there with me through all these years. Joining a program which was as unique as EHD wasn’t easy and I am glad to have gone through this journey with them by my side. CEH will always hold a special place in my heart owing to CHD2K14. I would like to specially acknowledge Aditya’s contribution in helping me shape this research from start-to-ﬁnish, co-presenting and authoring papers with me, and for being a fun outlet of stress busting every time things got overwhelming. I am also very grateful to Anurag for giving valuable feedback to the work and for our general discussion around academic practices, something I really cherish. Thanks to my dual degree friends, Riya, Nikita, Shreya, Swaraj for making the research years and KCIS lab days fun. It made the additional year in college all worth it. My college years would have been in- complete without Shruti, Auro, Mukul, Natasha, and Ameya, organising the most fun and offbeat plans, only rivalled by American sitcom shows.

Lastly, I am very fortunate to have an incredibly supportive family who have helped me, nurtured me and supported me on my path every single day. Thanks to them for giving me the conﬁdence to pursue ideas and charter paths which may not be typically traditional, and most of all, for seeing me as their little rockstar even on days I would doubt myself.

v Abstract

Last decade has witnessed a growing role played by online platforms in enabling socio-political movements and shaping narratives. Social networking sites have evolved into being active sites of political culture and begin to function as a “public sphere”. This has also enabled users on these platforms to conduct campaigns and coordinate acts; engaging in collective user practice of “trolling” or shutting down voices. We observed a recurring form of online collective action that played out on Indian online platforms. Users organise over Twitter to initiate a collective uninstalling/down voting of an app endorsed by a celebrity they have ideological disagreements with. The logic is simple: it is a call to boycott the product the associated celebrity is endorsing in hopes of causing economic or reputational consequences (poor review, low rating, uninstalls) to the company, forcing them to disassociate with the said celebrity. This would potentially scare public ﬁgures from taking stances on controversial issues which challenges their narrative.

We pick the case of an online boycott of an e-commerce application Snapdeal, initiated by the Indian Right on Twitter for our study. This boycott campaign involved calls to collectively uninstall, give poor ratings, and leave bad app reviews on App store, over their brand ambassador Aamir Khan issuing a controversial statement on increasing intolerance in India. As this statement didn’t sit well with the Indian Right, they sought to leverage reputational harm as a way to force Aamir Khan to ”apologise” for it, and also to create precedence to scare public ﬁgures on challenging the right-wing narrative in the country. We conduct a mixed methods investigation to unpack co-ordinated user practice and behaviour in such online campaigns. We investigate how platform features get appropriated to serve the goals of the campaign, and how tweet framing strategies were effective in mobilising and calling to action.

Through this thesis we make two contributions, 1) provide empirical results on user behaviour and tactics on mobilising for online collective action 2) theorise and present an argument how these campaigns are able to achieve very similar user goals as platforms designed for collective action do. We conclude with implications for design on building better online civic systems that rely on engaging large number of people and, how designers need to be cognisant of appropriation of features lest they become a moderation challenge.

vi Contents

Chapter Page

1 Introduction ...... 1

2 Background and related work ...... 4 2.1 Background of case study...... 4 2.2 Networked platform as a site of political culture...... 5 2.3 Consumer boycott behaviour...... 6 2.3.1 Online boycotts...... 6 2.4 Making sense of events with “theory of framing”...... 7 2.4.1 “Framing process” to unpack online movements...... 8 2.5 Collective action systems: in online space, challenges and e-petitions...... 9 2.5.1 Online collective action...... 9 2.5.2 Challenges in executing collective action...... 9 2.5.3 Online petitions: Collective action platforms...... 10

3 Large scale user practices ...... 11 3.1 Data Collection...... 11 3.2 Findings...... 12 3.2.1 @-mentioning : Targeting and identiﬁcation...... 13 3.2.2 Hashtags: Topical communities and messaging...... 14 3.2.3 Retweets: Polarisation and marginalisation...... 16 3.3 Conclusion...... 18

4 Tweet framing techniques ...... 19 4.1 Data collection from Appstore...... 19 4.2 Tweets: Framing arguments...... 20 4.2.1 Engagement metrics...... 21 4.3 Tweets: Framing calls to action...... 22 4.3.1 Engagement Metrics...... 24 4.4 Conclusion...... 24

5 Formalising online collective boycott system ...... 26 5.1 Data and methodology...... 26 5.2 Findings...... 30 5.3 Conclusion...... 32

vii viii CONTENTS

6 Discussion ...... 33 6.1 Moderation challenges due to emergent collective action...... 33 6.2 Opportunities in designing for collective action systems...... 34

7 Conclusions and limitation ...... 36 7.1 Methodology limitations...... 36 7.2 Design as a socio-technical enterprise...... 37 List of Figures

Figure Page

2.1 Tweet volume and event timeline...... 5

3.1 Indegree - outdegree for user handles...... 14 3.2 Hashtags: Ego network with top 2 hashtags from each category...... 15 3.3 Retweet network with two largest connected components...... 17

5.1 Content review of elements that make up one page of avaaz.org...... 27 5.2 Bar indicating current signature count and signatures left to go on different platforms. 29 5.3 Different petition platforms highlighting their recent signtories...... 29 5.4 Example of an artifact displaying solidarity from change.org...... 29 5.5 Comparative table of goals, and parallel features between two systems...... 30

ix List of Tables

Table Page

3.1 Hashtag categories and hashtags...... 16

4.1 Argument category and engagement stats (µ:mean, σ:standard deviation)...... 22 4.2 Descriptive statistics for different frames in calls to action...... 23

5.1 Codebook highlighting features found on different petition platforms...... 28

x Chapter 1

Introduction

Social media has altered the landscape of human communication, changing the way we seek information, collaborate and build our networks. The last decade has seen a tremendous rise in the number of platforms that help us build our personal and professional network and connections. The spectrum of the nature of these platforms range from less personal, micro-blogging platforms such as Twitter, to more intimate, restricted sharing platforms such as Facebook. Consequently, Social Networking Sites (SNS) have also begun to act as a “public sphere” [69] [15]. Public spheres are sites of discourse and debates, often gathering people from all walks of life to opine on topics of social interests to influence political and collective action. As the famous German philosopher, Habbermas mentions, this public sphere could be a real space such as a town hall, or city’s central square, or an imagined or virtual community of people [13][34]. When we analyse social networking sites (and other online platforms) as a “public sphere”, we are able to make sense of the vast range of activities that happen on them. We observe how they have become sites for crowd- funding, petition signing, political messaging, job searching, or even serve as “mini-Googles” [55] where Facebook groups are leveraged as information seeking sources from people who are part of it. Development of information and communication technologies (ICTs) and social networking sites have reduced the barriers of outreach [35], and cost of communication making it easier for users to reach out to a vast number of people at a very low cost [35][7], making it mainstream as a “public sphere”. This has established SNS as an important ground of political culture. In this thesis, we attempt to unpack one such political phenomena which exemplifies the growing role of social networking sites as a public sphere. We are interested in a specific kind of political activity where users on online platforms are able to effectively mobilise and execute collective action to set a political narrative. We observed a recurring form of online collective action that plays out on Indian online platforms. Users organise over Twitter to initiate a collective uninstalling/down voting of an app endorsed by a celebrity they have ideological disagreements with. The logic is simple: it is a call to boycott the product the associated celebrity is endorsing in hopes of causing economic or reputational consequences (poor review, low rating, uninstalls) to the company, forcing them to disassociate with the said celebrity. This would potentially scare public figures from taking stances on controversial issues

1 which goes against their ideology. Thus, this is a tactic of utilising online collective action to strong arm counter opinion and build a favourable political narrative. Studies on online collective action have documented instances of mass altering of reviews of book [67] collective doxxing and harassment of female journalists and gamers over Reddit and Twitter [49], and how certain subreddits such as r/The Donald are fertile ground for mobilising Redditors to engage in activities online such as rigging online polls [25]. Our work is situated in understanding these practices [11][9] which pose challenges in effective platform moderation. We pick the case of an online boycott of an e-commerce application Snapdeal, initiated by the Indian Right on Twitter for our study. This boycott campaign involved calls to collectively uninstall, give poor ratings, and leave bad app reviews on App store, over their brand ambassador Aamir Khan issuing a controversial statement on increasing intolerance in India. As this statement didn’t sit well with the Indian Right, they sought to leverage reputational harm as a way to force Aamir Khan to “apologise” for it, and also to create a precedence to scare public ﬁgures on challenging the right wing narrative in the country. Recurring instances like these demonstrate how social media platforms are getting appropriated by political actors through coordinated campaigns by utilising their technological affordance. Through a mixed method study, we conduct two broad investigations - 1) What are user practices in calling to boycott, communicating with other users, and amplifying their narrative 2) How are the users framing their tweets to enhance mobilisation and encourage participation. The ﬁrst investigation is conducted through a quantitative and graph visualisation method where we analyse large scale behavioral patterns. The second investigation is conducted through a qualitative analysis of the tweets, utilising a sociological lens of “frames” and theory of framing [cite]. We identify the different ways tweets were framed to persuade and engage with Twitter users sympathetic to the cause, and call them to action (uninstall, rate poorly, leave a bad review). We provide a brief timeline of events as they unfolded in the ”Snapdeal-Aamir Khan controversy”, in our chapter giving background2. Inline with our research motivation, we shape our research questions in the following way:

RQ1 - What are the user practices at scale, of participants in “Aamir Khan/Snapdeal” controversy? RQ2 - How are tweets framed to engage and mobilise participants to call to action?

From our investigations in this study, we uncovered user behaviour and framing techniques that enabled boycott participants to efﬁciently disseminate their message, and get people to participate in an online collective action. We found that technological affordances provided by Twitter and Appstore were important factors in determining how the campaign shaped up. Theory of technological affordance [17][22] states that users interaction with a technological artifact is a function of what they want to get done, coupled with what the technology affords them to do. This leads to emergent and situated action by users in those constraints and perceived technological capability [73]. To better contextualise our ﬁndings of collective behaviour practices, we aimed to break them down into user goals, and how they were able to achieve them at different points of the campaign. We intended to draw inspiration from col-

2 lective action systems which are explicitly designed to support these needs and behaviour, and identify the features they contain and the affordance that they provide. Then, make parallels between features on these platforms and what it lets the user inform or achieve, and ﬁnd if we have anything similar in practice of “Aamir/Snapdeal” boycotters. We pick online petitioning system as the choice of our system that is designed to support organising and executing collective action. Petition websites have grown in number over the past [1][8][80] decade and have been widely adopted by Governments with them building their own petition signing platforms [43][10]. This has ensured that petition platforms have evolved into mature socio-technical systems, designed to support people’s engagement in the political process. Additionally, like in our “Twitter/Appstore” study, their user journey of creating of petition, sharing, signing are entirely online. Inline with it, we frame our research question as -

RQ 3 - What are the affordances provided by online petition platforms, and how do they compare with what boycott participants were able to achieve in their campaign?

We find that participants in the boycott campaign were able to achieve largely the same goals that petition platforms provide. We argue that, in absence of explicitly designed systems, existing platforms get appropriated to suit these needs as is deemed fit in the emergent context of collective behaviour. This often leads to unintended challenges in moderation of social media platforms, where harassment, doxxing, marginalising of opinions could become very common, defeating the purpose of it being a “public sphere”. Sourcing our findings and learning, we offer lessons to designers on being cognisant when building features that rely on crowdsourcing for aggregated information with their potential to be weaponised for unintended goals. We also offer opportunities in designing online civic systems that engage users better and help governments in situations such as fundraising for natural calamities. We conclude with limitations and future work in our final chapter.

3 Chapter 2

Background and related work

In this chapter we begin by outlining the background of our case study with timeline of the events. Related work begins with a section where we talk about the growing metaphor of social media as a “public sphere” and how online platforms are becoming active sites of political culture, narrative building and discourse which has ramiﬁcations on real world events. Next section mentions the history and role of consumer boycott behaviour as a socio-political tool, the recent avatar of online boycotts and how HCI literature has studied them. We discuss the sociological theory of “framing” as a communication device and it’s role in social movements. We also look at related work in HCI on how “framing” has been applied to study online social events and phenomenon to derive inspiration for our study. In the last section we discuss, what constitutes online collective action, their literature pertaining to Human- Computer Interaction (HCI) and Computer Supported Co-operative Work (CSCW), and challenges that concern executing collective action from classical sociological theories.

2.1 Background of case study

We provide a brief timeline (ﬁgure - 2.1) on the events as they unfolded in the Snapdeal-Aamir Khan controversy.

1. 23 Nov 2015: Aamir Khan, a popular ﬁlm celebrity from India, makes a remark on the ”growing intolerance in India” and how his wife (Kiran Rao) suggested moving out.

2. 24 Nov 2015: Statement picked up by the media and Twitter, leads to outrage over the said remark. ”Nationalist” sentiments claim to be hurt over the remark.

3. 25 Nov 2015: Twitter users troll Aamir Khan and begin a call to boycott Snapdeal (a brand endorsed by Aamir Khan). Users begin to write poor reviews, giving poor rating and uninstalling the app with the hope to leverage power as consumers and build pressure over Snapdeal to remove Aamir Khan.

4. 7 Jan 2016: Aamir Khan loses Govt. of India’s Incredible India ambassador contract

4 Figure 2.1: Tweet volume and event timeline

5. 5 Feb 2016: Snapdeal does not renew Aamir Khan’s contract.

This comes under the backdrop of a growing rightward shift in the Indian political scene with the victory of the Bharatiya Janata Party (BJP) [59], whose regime the actor sought to criticise. The Party is also known to have a number of vocal supporters who sympathise with the “Nationalist” cause [16] [60] and “Hindutva” [74]. Aamir Khan, a prominent Muslim celebrity being vocal about his fear and apprehension is seen as an attack on the government, with suspicion over his motives on making the statement.

2.2 Networked platform as a site of political culture

Manuel Castells’ [36][14] highly cited work of the past decade explores the different ways in which information and communication technologies (ICTs) act as drivers for creating relationships and networks which could not have possibly existed before. With low barrier of distribution and lowered opportunity costs of connecting with one another, ICTs aid de-centralisation of access and control and make it easy to broadcast one’s view into the network and gain recipients. This is especially useful when members in this network need organising, or execute actions that require collective effort. They aid mobilisation and messaging efforts through affordance of large scale and fast communication, helping people with similar activist agendas to connect and pool resources for collective action [14]. Without a networked culture and infrastructure, they would have to overcome barriers in outreach, communication, mobilisation and collective action (which still persist, but to a lesser degree). Hence with the rise of networked society, it is unsurprising that social media and online platforms are becoming important determinants of socio-political realities of our times. We have seen this play out in elections in India,

5 where the BJP government’s rise to power was fuelled by social media outreach and campaign, often countering traditional media’s narrative and executing successful rebrand and direct electorate reach [58][59]. As we also see from the US, the 2016 presidential election was marred with controversies over Russian interference through bots [4][5][3], and (dis)information operations and campaigns [71][2][72]. Thus online platforms and social media sites such as Twitter, Facebook, Reddit are increasingly being used by a section of politically inclined users to set agendas and facilitate actions supporting their political narrative. A few recent examples of political outreach and collective action that were fueled by social media and contained strong digital traces were #BlackLivesMatter, #OccupyWallStreet, Arab Spring, #JeSuis Charlie, amongst others. Research explores how users engaged with these movements via social sharing of pictures, live event streams of protest sites, event updates, mobilisation and utilising these internet platforms as a pressure building exercise [50]. Live tweeting and reporting through social media were considered more reliable than newspaper reports. Faster dissemination of information through social media, also helped in better mobilisation and co-ordination of activities [50]. Through social media, people with similar stake and activist goals could come together to pool their resources and devise strategies(Schradie, 2018). While not everyone was an active participant and some were labeled as slacktivists too [44] (only online participation and no on ground contribution), social media helped reach out to a wider array of people with quick, and very personal messaging. Thus, events that happen ofﬂine have a direct spillover in the online and vice versa, making research of these phenomenons important.

2.3 Consumer boycott behaviour

Boycotts have long been used as a tool to build pressure and leverage power as consumers, to force companies to make different choices in their business policies. It is even termed as voting with your wallet [38]. Though the efﬁcacy of this tactic is contested [65], [56], that has not stopped different activist groups from issuing calls to boycott over issues of environment, fair pay, ethical treatment of animals amongst other popular issues [38]. The website, www.ethicalconsumer.org keeps a track of companies which have been called to boycott over different “ethical” issues. Boycotts can also involve actively choosing to buy products of the rival company that one is boycotting (termed, buycott [33]). Studies suggest, for a lay person, building a list of brands that one can choose from seems to be more effective than building a list of brands that one cannot choose from [56].

2.3.1 Online boycotts

As discussed before, SNS are becoming new sites of political culture, and boycotting trends haven’t been left far behind. Many such boycott campaigns are started online where they gain traction for collective action [45]. A popular #DeleteUber trend objecting to Uber’s promotional campaign related

6 to them profiting from a protest against the US president’s immigration ban lead to a severe backlash and a subsequent change in policy by Uber. There have been other similar online campaigns such as boycotting the National Rifle Association 1 , and Netflix boycotts 2. There have also been a few sustained boycott campaigns such as Grab your wallet 3, and long running boycott petitions. In the Indian context, we identified four such (major) events where a celebrity made a controversial statement which a section of the twitter populace took objection to and in-turn made a call to boycott the product endorsed by the celebrity. While these calls were general product boycott calls, they also involved uninstalling and/or down-voting the app on Google and Apple’s app-stores. These boycotts campaigns targeted Snapdeal (an e-commerce platform), Amazon (e-commerce) 4, Republic TV (news channel app)5and Snapchat (social networking app) 6. These boycott related online behaviours have been increasing in recent time, but little is known about their user behaviour and their operatives from a platform design perspective. Our study contributes to primarily understanding this user behaviour. In HCI literature, recent research has focused on overcoming some of the challenges that underline organising successful boycotts. Li et.al [45] designed a light and semi-automated prototype “Out Of Site” which eases boycotting goods by restricting web searches through browser extensions. Work by Hanlin li et.al [45], identified the motivations, tactics, roadblocks and challenges in protesting technology companies through non-use in the US [46]. Their design implications highlight ways to support protest non-use such as query (e-commerce, informational) re-routing to alternate players, or browser extensions that generate “fake” user data and obfuscate digital traces. Mills et.al [52] analysed the effective ways in which users on Reddit were able to oppose and boycott SOPA’s (Stop Online Privacy Act) provisions and build an information base through reddit’s front pages. Our study contributes to this growing body of research by unpacking boycott practices, and how they are aided or constrained by platform design.

2.4 Making sense of events with “theory of framing”

In sociological studies, the theory of framing is the tenet that ideas and reality are complex, and dense, hence reality gets construed into “frames” [28]. Frames help people make sense of the world around them, and ascribe meaning to the events. For example, the debate around abortion is multifold, but the use of word “foetus” v/s “dead-baby” ascribes different interpretation to it, and hence presenting the same issue in different frames. In the context of social movements, there are multiple actors, stake- holders, participants who come from different standpoints. What they highlight in the event, and which part they omit or counter, are termed as “competing frames”. All these actors are competing to get

1https://www.nytimes.com/2018/02/27/business/nra-boycotts.html 2https://decider.com/2017/02/09/dear-white-people-triggers-netﬂix-boycott/ 3www.grabyourwallet.org 4https://bit.ly/3jI2b9o 5https://bit.ly/30NM2Xn 6https://bit.ly/30W9ZvY

7 their frames as the established frame. Newcomers make sense of the movement through the established frames, and pick their sides. New information is processed and established frames are challenged with counter frames as time progresses. But as we note, the vantage point of competing and point of sense making are the frames, implying their importance. As Goffman argued, frames help to “locate, identify, understand and navigate” world and social situations at large. Bennett et al [15] argue that successful social movements often employ messages with an action- oriented framing. Alongside, there is a process which coalesces smaller frames, into bigger frames which are more palatable for wider consumption [61]. This process identifies central tenets being addressed within the movement and urges actors within it to act on repairing/responding to it. It allows actors to emotionally or rationally relate to a socio-political situation (movement) which is in dire need for collective action [50][14]. Influential actors pick up on these framed messages, and build a narrative that must effectively mobilise other actors into an action. Thus we see that frames play an important role in how people make sense of competing narratives, rising out of structural or non-structural forces [28]. In the context of social movements/collective action, they perform the process of condensing the complexities in ways such that it mobilises people, garner support, “demobilize antagonists”. Social movement scholars view them as much more than just sources of text. They are niches within which users actively engage with ideologies and with each other in order to allow for a continuously evolving definition of the social movement.

2.4.1 “Framing process” to unpack online movements

In the past few years, the process of framing has been effective in explaining social movements with digital traces like #BlackLivesMatter, #OccupyWallStreet, #JeSuisCharlie, #Kony2012. HCI and CSCW research has shown interest in understanding evolving frames of such movements online [2] [78][72]. [72] utilised an integrated networked gatekeeping and framing lens, to examine how #Black- LivesMatter frames were created and contested by supporters and critics on the political left and right. They analysed how some “frames” make it to the mainstream discourse, while a few others don’t. This is termed as networked gatekeeping. Meraz et.al (2013) [51] analysed networked framing in the context of the Egypt uprisings in 2012, and how signiﬁcant frames were “revised and rearticulated” by elites and non-elites. Similarly, other work by Wilson et.al and Arif et.al [78][2] studied how frames can function as ways for “information operations” to undermine the traditional information systems and manipulate civic discourse. There is a large body [6][28][61] of work which discusses how frames are created, disseminated and interpreted in the online space. For instance, research shows us how during #BlackLivesMatter the initial frames which emerged were regarding police brutality, gave rise to a parallel discourse countering this framing and messaging in the form of #BlueLivesMatter [72]. Hence, the discourse and the counter discourse were both formed in the light of the frames initially adopted to sustain the movement. As social networking sites allow different frames to gain prominence, and messages can be framed with pictures, videos, hashtags to support or denounce a particular narrative, we utilise this theory

8 to make sense of the user generated content for the “Twitter/App Store study” and “online petitions study”. We utilise the “framing process” to unpack different strategies and techniques in presentation of messages and dissemination of information.

2.5 Collective action systems: in online space, challenges and e-petitions

2.5.1 Online collective action

As discussed before, online platforms and SNS have been instrumental in mobilising efforts for social movements such as Occupy Wall Street, Arab Spring, Black Lives matter [cite] etc. We observe that they don’t just play a role in mobilising for offline collective actions but users on these platforms are engaging in collective action online, with large socio-political implications [24][49][67]. Online petitions are a classic example of a system designed for online collective action and to signal support and critical mass for a change [81]. Nicholas’ work [76] explores the efficacy and tradeoffs in organising collective act of “data strikes”, for example, users not engaging in practices such as “reviews and ratings”, deleting their personal data to “harm” recommender systems, to build a leverage against big internet companies to alter their practices. Research in HCI [52] has investigated how subreddit moderators partake in collective subreddit “shutdown” practices to negotiate Reddit’s platform policies. Well documented online practices like posting selfies for collective reclaiming of identity and fighting against stereotypes [47] and other identity hashtag movements are examples of attempts at social justice through crowd participation by marginalised groups. Like e-petition systems, online crowdsourcing systems for civic engagement have been researched by practitioners in HCI which can bridge the gap of policy making and lived experience of the public [32]. Online collective action has also emerged as a method where the majority through their sheer number of active users online could attempt to marginalise and silence minority voices. Study by Flores et al [24] highlights how the subreddit r/The Donald is utilised as a ground for mobilising the redditors for actions online such as rigging online polls, or collectively trolling people of different political incli- nations. There have also been instances of collectively altering the reviews of a book [62], collective doxxing and harassment of female journalists and gamers [49] on reddit and Twitter. All these incidents highlight how groups of users utilise the affordance provided by online platforms to collectively pursue their ideological agendas.

2.5.2 Challenges in executing collective action

Executing successful collective action is a challenging task. Research in sociology and allied do- mains have highlighted the problems underpinning mobilisation leading to collective action. Executing collective action suffers from challenges such as the free rider problem [31], tragedy of commons [70], prisoners’ dilemma [40] and challenge in communication of goals and development [57]. In the free rider problem, while the beneﬁts of the collective action are shared by all participants, participants may

9 not have the incentive to participate individually, expecting the other members to put in the work of organising, and collective action. Similarly, prisoners’ dilemma highlights the challenges in co-operating for collective action, without receiving co-operation in return from other participants and bearing the risk of lost effort. Both these challenges are variants of conﬂict between maximising one’s and collective utility. Often, executing collective action requires that the participants are motivated enough, see a purpose in participating and are on the same page on the whereabouts of the movement. This is a massive challenge in communicating the agenda, progress of the movement and changing developments to all the diverse participants who are involved in different ways. There are well reported nuances like participants being more likely to contribute to a cause if they are aware that it is expected to succeed [57] . Devel- opment of ICTs have played a crucial role here by reducing costs of communication, ability to convey progress, and ease of reaching out to diverse array of participants [36]. Near real-time communication, also lets participants weigh out their beneﬁts, and risk of participation in collective action.

2.5.3 Online petitions: Collective action platforms

Online petitions are growing in popularity with their increased adoption by governments as well as private entities which act as platforms. These platforms are known to help with increasing reach, riding over the ICT infrastructure that enables petitions and causes to reach a wide segment of people. Many governments have formal channels for acknowledging and responding to ofﬂine petitions and signatures which have been adapted to the online context. For instance Govt of UK runs, petition.parliament.uk, while Govt of USA runs petitions.whitehouse.gov. These platforms also apprise collective action challenges [43][29][81][80] and help mitigate lack of social proof with signature counts, risk of wasted effort with the increasing liklihood of a petition gaining signatures and a response by the concerned authority. Research in HCI [43][29][20]has shed light on what makes a power user for petition signing, what are the factors leading to successful petitions, how ‘slacktivism’ relates to activism.

10 Chapter 3

Large scale user practices

In this chapter, we present our ﬁndings on large scale behaviour patterns of users engaging in “Aamir Khan/Snapdeal” controversy to drive their narratives through the use of affordances provided by Twitter: Retweet, @-mentioning and hashtags. We begin by outlining our data collection process, then explain the affordance that Twitter provides its users, then present our ﬁndings and conclude this chapter. The research question under study in this chapter is -

RQ1 - What are the user practices at scale, of participants in “Aamir Khan/Snapdeal” controversy?

3.1 Data Collection

Gathering: We gather our data for our analysis by running the Twitter API and employing other search and retrieval techniques such as using Twitter Scraper - an open source front-end retrieval tool which performs queries on Twitter’s advanced search platform. Since, the twitter API doesn’t retrieve all the data, it was important to additionally engage with other methods of extraction.We gather tweets for the controversy from 20 November 2015 to 3 March 2016, using a seeding process. We initiate gathering tweets with seed hashtags of #AamirKhan and #Intolerance. As and when the tweets come, we increase our set of hashtags and add the ones that co-occur with the seed hashtags like #BootOutSnapdeal, #AppWapsi. #IStandWithAamirKhan and #IntoleranceDebate. We also ran boolean queries such as ”aamir AND snapdeal”, ”aamir OR intolerance”. It is also noted that running a search in the scraper or the API returned results for substrings and were non case sen- sitive. Searching for "#Aamir" returns results for aamir, AAMIR, #Aamir. We converted the tweet into a lower case string, and tokenized them using Stanford’s NLTK library [48]

Finalising: In order to ensure that the tweets used in our analysis reﬂect our lists in our topics of interest, we ran it through our own post-ﬁltering process. We run our tweets through a regular expression built to check if the tweet contained at least one of our keywords and hashtags. All these tweets were

11 then selected in our data set.

Dataset statistics:The data set consists of 117632 number of tweets, by 39461 number of users. Of these 52127 numbers of tweets were NOT retweets (but include quote retweeting). This doesn’t imply that tweets which were not retweets were unique tweets, as we also observed that there were tweets which were copied across handles (indicating co-ordination and spamming). The schema of our data set was - "tweet", "handles which retweeted", "handles which favourited", "flag indicating whether retweet or not", "number of retweets", "number of replies", "number of favourites"

3.2 Findings

To analyse the behaviours that are indicative of collective and coordinated actions, we explore the affordance provided by Twitter to users and how they got appropriated in a collective setting. As literature in HCI argues, the possibilities that a technological product creates can be partially explored through the signiﬁers and “features” they possess [17]. We identify the three widely used Twitter features - the @-mentioning feature, the #-hashtags feature and the RT- retweeting feature. We elaborate on what the features look like, “signiﬁers”, what the feature is expected to achieve.

1. Hashtags - Hashtags, whose signiﬁer look like “#” immediately turn the text next to it blue on Twitter. Blue also makes the fact apparent that it is clickable, and can lead to some other place on the platform, i.e, it redirects to some other link. #-hashtags are usually topics of interest and are also intended to align the tweet to a broad audience. for example, a tweet - “Listening is important for empathy. Without empathy there is no design. Without design there is no technology #ux #designTwitter #designEducation”

Hashtags such as “#ux” identify the topic of interest, whereas #designTwitter relates to a broader community of interest who could reach this tweet when they click the hashtag, People add hashtags for a variety of reasons, a few for distilling their thoughts, others to reach a wider audience, or to even inform their follower base of which topic the current tweet lies in. Hashtags are manually added in a tweet text and is a creation feature and not a feature which captures a user’s reaction to it.

2. @-mentions - One user on Twitter, can “@-mention” the other user through their handle names. It automatically makes the handle clickable and an account that can be redirected to when someone clicks on it. Usually when user 1, @-mentions user 2, like “Hey, @user2 is not returning the money they owe the Govt and leaving the country to relocate somewhere else” - whoever clicks the handle now is aware of who is being talked about, and user2 is also informed that they have

12 been talked about in someone’s tweet. Like hashtags, they are added to a tweet text and are a creation feature.

3. RT - retweets - Retweets is an explicit Twitter feature with its own symbol which informs the user that it can be acted upon. It is not a creation feature, and once selected displays the tweet to all the followers. It is used for ampliﬁcation of a certain opinion - both negatively or positively, with a hope of an impact or gathering social capital, or status.

4. Fav - favourites - Like retweeting, favoriting is not a content creation feature, but rather something which captures a user’s reaction to a tweet. Unlike retweeting, it is not meant for endorsing/criticising/amplifying the tweet’s position because it doesn’t automatically expose the tweet to the follower base. The purpose of favoriting is the same way “likes” work on facebook - to let the creator of the content know that they were amused by it.

5. Keeping these features in mind which dominate the Twitter narrative, we analyse the @-mentioning behaviour (users tagging other users) and hashtags used and operated at scale. We also analyse evidence for possible marginalisation and amplification of specific voices on the platform through the use of Retweets, RT function. Since favoriting isn’t a content creation feature, nor amplification, we don’t focus our analysis on favoriting behaviour. We conduct large scale quantitative analysis coupled with data visualisation for our analysis.

3.2.1 @-mentioning : Targeting and identiﬁcation

From our entire dataset of tweets, for every tweet we identify the user handle tweeting, and the user handle they are mentioning in their tweets. We plot a scatter plot graph, with in-degree on the x-axis, and out-degree on the y-axis. Borrowing from social network analysis (SNA) methods, in-degree for an account refers to the number of twitter handles mentioning them, and out-degree refers to the number of Twitter handles they mentioned. For this we gather the entire list of handles prevalent in our dataset, and then find how frequently they co-occur with each other. From fig-3.1 we see that both @snapdeal and @aamir khan have very high in-degrees (26134 and 12674, respectively) and 0 out-degree. This reflects that, from all the tweet handles in our dataset, they were mentioned by 26134 and 12674 handles respectively and they mentioned no handles in our dataset. Hence, of all the users who were in the fold of controversy - neither Aamir or Snapdeal replied to them but they were targeted very heavily. We also find the handle, @iamsrk as an outlier with high in-degree and 0 out-degree. Understanding the socio-political context, Shah Rukh Khan was dragged into the controversy as being another fellow Bollywood star identifying as a Muslim. The other group of outliers we see are the handles with 0 in-degree but very high out-degrees. We identified 10 such handles. On careful examination of the user profiles, 4 of them appeared to be bots (DFR, 2017) (guidelines - only retweets, same tweet multiple times). They were massively tagging @snapdeal, and @aamirkhan and news media handles such as @ndtv. The other 6 accounts,

13 Figure 3.1: Indegree - outdegree for user handles

didn’t appear to be bots but were consistently tagging @snapdeal, @aamirkan and other a couple of other users and drawing their attention to the controversy. On an average each such handle was tagging 50-60 other user handles. The third kind of user handles that we see in the ﬁgure are the ones with an average indegree of 2 ( σ = 0.2) and an out-degree of 5 ( σ = 10). These were about 97% of the total user handles. We observe that there is very effective targeting that the users are engaging by ways of @-mentioning. Conventionally, Twitter mentions are conversational in nature where by the mention of @-userhandles, users draw attention of one another to a particular tweet. Here we ﬁnd that instead of an intended con- versation, the ”@” + username mentions were used to identify the intended targets and employ what we call as collective targeting. There is a clear establishment of who the targets are, and this information is being disseminated in the network.

3.2.2 Hashtags: Topical communities and messaging

From the tweets in our dataset, we see the occurrence of the hashtags in tweets. We plot the hashtag and their frequency. We see that just 13 hashtags capture 98% of the tweet volume. In these hashtags we analyse two things -

1. What is the lexical nature of the hashtags and how are they utilised?

14 2. If we call all the user-handles using a certain hashtag as a community, how overlapping these communities are and what does it inform us?

Figure 3.2: Hashtags: Ego network with top 2 hashtags from each category

For these 13 hashtags, we code and classify the hashtags and outline the guidelines in table - 3.1. The hashtags, categories of these hashtags and the percentage are displayed in the table.

Calls for action hashtags : Hashtags which fell into this category contained verbs directing people to do an act of rebellion such as "bootout", "AppWapsi"(return the app), (Say)No", "uninstall", "boycott". About 42% of tweets employed the use of such a hashtag. Opinionated hashtags : The lexical nature of these hashtags was such that they expressed an opinion on the issue. For instance hashtag such as #AamirInsultsIndia is an opinionated hashtag. Only about 21% of user used one of these hashtags. Topical hashtags : These hashtags were neutral and expressed a large topic of interest in the controversy such as #Aamir, #Snapdeal. About 82.1% of tweets contained at least one hashtag from this category.

15 Category of hashtag Hashtags Percentage

Notosnapdeal, boycottSnapdeal, bootoutSnapdeal, Call for action 42% AppWaapsi, shameaamir IstandWithAamir, AamirRightOrWrong, GetWellSoonPK Opinionated 21% AamirInsultsIndia Topical Intolerance, AamirKhan, Snapdeal, Intolerancedebate 82.1%

Table 3.1: Hashtag categories and hashtags

We also build ego networks for all the top 20 hashtags. At the center of each ego network is the hashtag, and the nodes are all the user handles who used the particular hashtag. For easy representation purposes, we pick the top 2 hashtags from each category as depicted in figure 3.2. We observe that #AamirKhan and #Intolerance are both large ego networks and are also overlapping. Thus, the sentiment of intolerance was made to ride over Aamir Khan and build a strong association between the two entities. Hashtags aren’t to merely indicate topics of discussion but also to disseminate information on the platform. We also observe that there is a significant (63.2%) overlap between #BootoutSnapdeal (call for action hashtag) and other hashtags. Similarly, other calls for action hashtags such as #Appwapsi, #BootoutSnapdeal all co-occur with other topical hashtags such as (#Snapdeal, #Intolerance) and have overlap of greater than 50%. We observe that use of hashtags creates small and temporary communities between the users of the said hashtag, with each other. In such a mobilising setting, we see that hashtags are employed to build and inform association of one entity to the other such as - Aamir Khan and intolerance. Use of hashtag of one category, significantly overlapping with use of another hashtag indicates piggybacking, where one bit of information seems to ride on another closely associated information which is disseminated around the social network. Calls for Action, which are commonly observed in collective action settings to direct participants to engage in an act, are informed here through means of“calls for action” category of hashtags.

3.2.3 Retweets: Polarisation and marginalisation

We analyse whether there were user behaviour patterns which were indicative of marginalising the alternate voices, and increasing tweet volume content to amplify the voice of the group calling for boycotts. We ﬁlter the tweets’ contents which were more than 50% similar to each other. These tweets were possibly made by bots, or by users running multiple accounts looking to increase the volume count of tweets in the controversy. We ﬁnd that there are 12 such spammed messages spanning 38 user accounts. There is also higher than average URL usage amongst the tweets (compared by running twitter stream API for an hour and comparing it to the 1% of tweets captured)

16 Figure 3.3: Retweet network with two largest connected components

Since retweets are methods of amplification, and also method of dissemination of information through the network to users with like minded agenda (such as followers) [82], retweeting patterns give us insights into which group of users cluster together and how ”loud” is their voice. It is also indicative of polarity on the platform [26]. Polarity also leads to marginalisation of alternate voices. We analyse if there was polarity on the twitter platform during the controversy and what kind of voices were amplified. We use a graph partitioning algorithm [27] which gives us partitions in the network based on a controversy score. We feed to it the retweet pattern between 2 users. If user1 retweeeted user2, there was a direct edge between them. The algorithm partitions the network into two largest connected components with 71.63% nodes and 26.22% nodes. We visualise an undirected graph using a force directed algorithm Gephi’s ForceAtlas2 From figure 3.3 we observe that the largest connected component on the left is much bigger (71.63% nodes v/s 26.22%) and is much denser too. For the purpose of our study, we pick the top 0.02% handles which make up approximately 73% of the total tweet volume in the network. These come out to be about 65 handles. Analysing these handles we find that 7 handles were of news-outlets. 49 of these handles were engaged in anti-Snapdeal, anti-Aamir Khan sentiment and were a part of the cluster on the left. Only 9 of the most retweeted handles spoke either in favour of Aamir Khan, Snapdeal or were neutral in their stance. The bigger, denser cluster on the left is the one engaged in collective boycotting behaviour. We observe that they are more interconnected, and amplifying each others’ voices. This marginalises and shunts the alternate voice, and possibly sets the agenda on the platform.

17 3.3 Conclusion

The like minded users and sympathisers of the “Nationalist” cause seem to have appropriated the platform to disseminate information about their goals and co-ordinate their activities. There is a clear targeting of Snapdeal and Aamir Khan, by way of @-mentioning. They are being @- mentioned to draw their attention to the issue and take a stand, and also to inform the rest of the people in the fold of the controversy to build pressure on them by this tactic. People aren’t debating the merits or demerits of the controversy with each other, but are isolating the two entities for further targeting through @-mentions. It acts as a way to very clearly establish who the opponents are. Information around mobilisation, is also being disseminated through the use of hashtag. Hashtags are both emotive (#AamirinsultsIndia), and also calls to action (#boycottSnapdeal). They also often co-occur with hashtags used by a large number of people such as Aamir and Intolerance which are general hashtags. This tactic seems to be effective in promoting new and growing hashtags such as ones calling for boycotts, and bringing them into the center stage of the controversy. They also serve as mobilising grounds as they bring people using different but similar hashtags together - forming mini topical communities on the platform. Similarly, such controversies also polarise the platform as has been well documented by studies [82]. This polarisation was utilised to amplify voice of boycotts, targeting and shunting down of alternate voices as we see from our retweet network. This is effective in setting agendas and works as a useful pressure building tactic.

18 Chapter 4

Tweet framing techniques

In this chapter, we present ﬁndings from a qualitative analysis of tweets investigating the different ways tweets were framed to mobilise and call participants to action. We further calculate engagement metrics through the use of “Retweet” and “Favouriting” numbers to identify which “frames” resonated with the boycotters and were effective. We conduct a two stage analysis. First, we identify different ways in which arguments against Aamir Khan were framed to mobilise participants to the cause. Second, we identify how participants were called to act on this apparent “wrong-doing” as a reactionary tactic. Both the analysis are conducted in a deductive thematic analysis paradigm [66], with quantitative weight assigned in measuring effectiveness between different ways tweets were “framed”. The research question under study is -

RQ2 - How are tweets framed to engage and mobilise participants to call to action?

4.1 Data collection from Appstore

To study the different framing techniques, and how they resonated with users who likely engaged in some level of boycotting behaviour, we extract a list of such user handles. Our metrics aren’t exhaustive, and they are limited by the ability to verify information on online platforms but serve as a useful method to study user engagement. We employ our list of user handles from our twitter dataset and gather users who also wrote reviews and gave poor rating on the App store (using Heedzy for App store reviews and ratings). We scraped this list with the belief that if a user handle is @manoj kumar, and they go by the name of Manoj Kumar on App store reviews, they are likely to be the same as they engaged in similar activity of tweeting in Aamir-Snapdeal controversy as well as uninstalling/downvoting as a part of the controversy. We only do this if “Manoj Kumar” (or any variations of it) occur only once. We also identiﬁed user handles (by means of regular expression search string) which made claims of uninstalling on twitter such as “Just uninstalled Snapdeal” and added it to our list of users who possibly engaged in collective action behaviour. A lot of such users also attached a screenshot along

19 with their tweet. Overall we ﬁnd 4236 users who partook in some degree of boycotting behaviour. There were 4521 handles. From these handles, we identify all the tweets from our dataset these users had ‘favourited’, or ‘retweeted’. These tweets come to a number of 7533. As we are interested in the framing of tweets which engaged these users, we pick this set of tweets. This chapter presents results on tweets framing arguments and tweets framed to call to action. The split between tweet counts are, 3216 and 4317 respectively.

4.2 Tweets: Framing arguments

Since this is a controversy where different arguments were posed to challenge and direct narratives (against or pro, Aamir Khan and Snapdeal), we aim to analyse what representive “framing” of the controversy appeared in our dataset. As mentioned in the last section, we compiled a list of boycotters who were active on Twitter, and also displayed evidence of participating in boycott activity. From these users we compiled a subset of tweets

Method of analysis: To do this we conduct a thematic analysis of tweets and code them deductively [66] using a “logical fallacy” framework popular in STS studies [18] . We call these tweets-based frames and then calculate user engagement metrics through RTs and Favoriting of such tweets. Two Masters level research students conducted the deductive analysis inspired by the framework. The tweets under study were 3216. They were agreed upon with a cohen’s kappa of 0.79.

We narrowed the frames that appear in our dataset into, what came up as six logical fallacies- (1) Denial (2) False Dilemma (3)False Equivalence (4) Suspicion (5) Whataboutery and (6) Anecdotal. We outline each category and present a representative tweet alongside.

Denial: Tweets in this category made an argument that if they (the user) didn’t feel that intolerance existed, then Aamir’s claim of intolerance was wrong.

“There is no intolerance as I can’t see it.”

False Dilemma: These tweets presented a false comparison in which one must choose.

“If there is intolerance then he should move to another country (Pakistan)”

False Equivalence: Such tweets made an unjust equivalence between being Hindu (his wife) and being safe because India is a Hindu majority country.

“Aamir Khan who has a Hindu wife, can’t feel unsafe in a Hindu majority country.”

Suspicion: These tweets cast suspicion over motives of Aamir Khan’s statement. Usual suspicions were over movie promotions, publicity or being agent of the opposition party.

20 “The intolerance remark is for publicity.”

Whataboutery: Such tweets engaged in whataboutery, a popular political tactic. These tweets aimed to shift the discourse from Muslim minority being under threat to cases when Hindu majority are unsafe.

“What about intolerance when Hindus are attacked.”

Anecdotal: These tweets sought to undermine the intolerance statement by citing an anecdotal evidence.

“If he (Aamir Khan) can freely cite his opinion, then there can’t be intolerance.”

4.2.1 Engagement metrics

As we are interested in which frames that emerged in the discourse set the tone for mobilisation and resonated the most with the boycotters, we analyse engagement metrics in terms of retweets and favourites. The descriptive statistics for all the arguments and their engagements are in table- 4.1. We see that denial and anecdotal arguments resonated the most in terms of both retweets and favourite counts. There was a statistically signiﬁcant difference between the groups for retweets (ANOVA (21.02, p < 0.05)) and favourite counts (ANOVA (41.2, p < 0.05)). This could be explained by their deep held beliefs which deny the existence of intolerance in the country, and their anecdotal experiences of not facing any consequences of effects of religious animosity. These frames hence align with their nationalist world view. This probably also encourages them to partake in action of boycotting to challenge the growing narrative of intolerance (propagated by Aamir Khan) by uninstalling/down-voting. Amongst retweets we see that, tweets in False dilemma were highly retweeted. Tweets under false dilemma where Aamir Khan was expected to make a choice between moving out of the country or accepting things the way they are, had very strong inﬂuence in their language too. This could explain the high retweet behaviour, as retweeting as a practice is also linked to the emotion conveyed in the tweet (Svelch et.al, 2016,). Amongst the tweets which were highly favourited, we observe that tweets under Whataboutery were highly favourited. Whataboutery as a political tactic is very common and resonates with people as a defensive response. This high favoriting behaviour could be explained by this. However, tweets which were framed as a Suspicion over Aamir Khan’s motives of making the statement weren’t received very well (either in terms of RTs or favourites). It could be believed that people on Twitter aren’t doubting the intention of making the statement but engaging with the argument itself and countering it with frames such as of denial of his experience, whilst countering with their own anecdotal experiences.

21 Framed argument Engagement Retweets µ: 156 — σ: 32.05 Denial Favourites µ: 321.07 — σ: 17 Retweets False µ: 340 — σ: 46.08 Dilemma Favourites µ: 266.78 — σ: 15.6 Retweets False µ: 18.02 — σ: 2.31 Equivalence Favourites µ: 24.8 — σ: 3.98 Retweets µ: 9 — σ: 3.6 Suspicion Favourites µ: 42.29 — σ: 7.8 Retweets µ: 91 — σ: 2.3 Whataboutery Favourites µ: 187 — σ: 21.3 Retweets µ: 228 — σ: 34.8 Anecdotal Favourites µ: 119 — σ: 23.1

Table 4.1: Argument category and engagement stats (µ:mean, σ:standard deviation) 4.3 Tweets: Framing calls to action

Now we identify the different strategies in which tweets were framed to call users to actively participate in the boycott. Our identifying marker for these tweets were the ones which mentioned “Snapdeal” or “playstore” or “uninstall” or “boycott” (and related terms).

22 Method of analysis:. We borrow the literature from Fleishman(1988) [23] and thematically and deductively code [66] our tweets in the dataset which relate to calls to action behaviour. We see that three primary frames emerge in our dataset. These three primary frames under our study are Direct call strategy, Progress visibility strategy, and Solidarity strategy. Cohen’s kappa was 0.59 with an inter rater reliability of 71.3%. The tweets under study were 4317 tweets.

Direct Strategy: These tweets made direct calls for action, and clearly outlined the steps on how to make an effective boycott statement by uninstalling/down-voting the Snapdeal app on the playstore.

“Go to Play Store, Select @snapdeal and rate them 1 Star * and comment that it is only because of @aamirkhan.”

Progress visibility strategy: Tweets in this category were the ones which were indicating real(or, fake) progress on the uninstalling and down-voting process. These tweets were intended to present a picture that a successful boycott movement was happening, and others must take part too.

“Wow 85,000 people angry with #AamirKhan’s hypocrisy, uninstalled @snapdeal app. #AppWapsi will hurt badly!”

Solidarity strategy: Tweets in this category seemed to indicate a solidarity amongst people who were against Aamir Khan, Snapdeal and had pro-nationalist sentiments and what they were supposed to do to avenge it.

“People who are united against #AAMIRKHAN statement must uninstall @Snapdeal.”

Framing strategy % users having RT at least once % users having Favorited at least once Median number of Users Retweets Direct Median: 521 Strategy 49.02 % 26 % Favourites Median: 121 Retweets Progress Median: 91 Visibility 38.09 % 37.2 % Favourites Strategy Median: 140 Retweets Solidarity Median: 340 Strategy 13.9 % 41.2 % Favourites Median: 221

Table 4.2: Descriptive statistics for different frames in calls to action

23 4.3.1 Engagement Metrics

We calculate which calls to action framing resonated the most with this set of users (the ones who engaged in some degree of boycott and uninstalling behaviour). We employ the use of retweets and their favourites to understand their engagement. In terms of retweets, we find that most of the users engaged with direct strategy at least once. 49.02 % of users retweeted at least one tweet from this category. In terms of favoriting, we see that the engagement is much lower than retweets for this category. Only 26% favorited at least one tweet from the direct strategy. This is possibly because, while retweeting a direct call might reach new users and encourage them to partake in the boycott, favoriting doesn’t amplify the tweet to all the followers of a user. Around 38.09% of users retweeted at least one tweet announcing some real (or unreal) progress of the movement (progress visibility strategy) while 37.2% favorited it. We see that the progress visibility strategy, trumps over the direct strategy in favouriting. Progress visibility encourages people, as it informs them that if they partook in the activity, the effort won’t go waste. Only 13.9% of these users retweeted tweets indicating solidarity (solidarity strategy). The favoriting count however was the highest amongst all other categories at 41.2%. It suggests that while users might not see much merit in retweeting a tweet with a call to action in terms of solidarity, the sentiment of solidarity resonates with them. Table-II outlines the descriptive statistics for each call to action styles. The median number of users who retweeted tweets in each category stand at - Direct-521, Solidarity- 340, Progress indicating - 91. We find that there was a significant difference between the strategies ANOVA (33.09 , p < 0.002). Direct strategy seems to have the highest engagement across all strategies. In favoriting behaviour, the median number of users who retweeted tweets in each category stand at - Direct-121, Solidarity - 221, Progress indicating - 140 .We find that there was a significant difference between the strategies ANOVA (45.02, p < 0.001). In favourites, Solidarity appears to be the most engaging amongst all the strategies.

4.4 Conclusion

From our analysis of the frames that emerged in our dataset, we see that the narrative of “intolerance” was challenged by the nationalist and political right on the platform by employing different arguments. These initial frames which came up in Aamir-Snapdeal incident, set the stage for potential action later. As Bennet et.al (2012) and Benford et.al (2000) [6][7] discuss, these frames appear and evolve through the social movements and are critical for participants to make sense of it. Tweets in frames of “denial”, and “anecdotal” category were highly personalised, which resonated with the boycotters. These personalised frames and relatability let itself to challenge the “intolerance” narrative and framework. Other frames like those of “whataboutery” were utilised to shift the discourse from Muslim unsafety to potential safety of Hindu majority. Tweets with aggressive tone, framed in “False dilemma” were highly retweeted indicating a strong desire to reject the counter narrative of fanaticism.

24 As we see that frames that appeared were highly personalised, and must have been a function of Twitter as a platform. 140 characters (as was the case in 2015/16), condensed complex socio-political narratives into personal agendas which emerged as broader frames. These initial frames of arguments and contention, carried themselves into setting stage for mobilising people into a collective action of boycotting/uninstalling. These when coupled with tweets framed as Calls to Action, possibly informed and disseminated information in the network on how to act on the grievance of the right wing sympathisers and users. “Direct Calls to Action” clearly outlining steps for uninstalling/downvote, as well as the ones “indicating progress” in the movement were important frames which seemed to resonate with the boycotters. They inform us of important frames that emerge in this stage of social movements where this translates into a collective action.

25 Chapter 5

Formalising online collective boycott system

In our previous chapters, we state the different ways platform affordance on Twitter and Appstore were appropriated, and tweet framing strategies adopted for boycotters to extend their message and direct people to action. In this chapter, we attempt to formalise these actions as an online boycott system. Borrowing from theory of technological affordance [22][73], we conduct a content analysis [39] of 5 e-petition websites to identify the affordances provided on these platforms and what purpose they fulfil. Drawing from our results stated in the previous chapters, we conduct a comparison on these two broad systems - the online petition platforms (explicitly designed for online collective action), and “Twitter/Appstore” online boycott campaign (emergent online collective action). This helps us formalise the acts, medium, and purpose of the boycotters on the platform, when we reference the features and purpose served through collective action systems like e-petitions. We find that “Twitter/Appstore” boycotters were able to achieve very similar goals in their campaign and fulfil their campaign motives through the “opportunity structures”. We discuss this in detail in this chapter. The research question under study is -

RQ 3 - What are the affordances provided by online petition platforms, and how do they compare with what boycott participants were able to achieve in their campaign?

5.1 Data and methodology

We choose 5 popular epetition platforms for our study, Avaaz.org, change.org, whitehouse.petitions.gov, ipetitions and the petitionsite. We conduct the design review utilising a qualitative research paradigm of content analysis. Content analysis involves analysing the different components that make up any piece of communication [39]. This communication could be a piece of “text”, website, video, or photograph. We inform our analysis with the steps outlined in HCI studies such as [54]. This analysis was conducted by 2 Masters’ research students who have prior experience in conducting qualitative research, including contextual inquiry, discursive studies and interviews.

26 Figure 5.1: Content review of elements that make up one page of avaaz.org

1. Website archiving

We visit all these websites on April 12, 2020 and save the snippets of their different pages. We conduct these visits on a Chrome browser and record them in PDF ﬁles. Doing this is important as these platforms change their design and functionalities from time to time and its crucial that we analyse how they appeared to us on the particular date.

2. Conducting visits on platforms

We conduct our visits on the website as logged in user, and as a new user on the platform. We interact with the platform by acts of signing a petition, sharing a petition on social media, creating a new user account, signing up for a newsletter, land by leaving comments under a petition. Both the researchers were mindful of the user interface that they were interacting with and noting all the features on platforms and their affordances.

27 Features Avaaz.org Change.org whitehouse petitions ipetitions thepetitionsite

Signature progress bar Yes Yes Yes Yes Yes

Call to action buttons Yes Yes Yes Yes Yes (sign/donate/volunteer)

Social sharing Yes Yes Yes Yes Yes

Petition by Yes Yes No Yes No

Petition to Yes Yes Yes Yes Yes

Artifacts that Yes Yes No No No encourage signing

Signing petition Yes Yes Yes Yes Yes as point of action

Last signed/ Recent signatories Yes No No Yes Yes

Updates and responses of petition Yes Yes Yes No No

Table 5.1: Codebook highlighting features found on different petition platforms

3. Developing a codebook

We conduct a ﬁrst round content analysis of these platforms where both the researchers inde- pendently develop their codebooks of the features and affordance they observed and interacted with.

These are accompanied with review material such as the saved snippets of the website with highlighted features, how the interaction with the feature was, what it signified, and what it let the user do. Both the researchers got together to share their codebooks and resolve differences if any, on interpretation of a feature, or a feature which was captured by one and not by other. We update our codebook post this iteration and conduct a second round of analysis. We revisit the websites, and calibrate and note features and interactions by constantly referring our codebook. We find that all the features that we listed in our codebook were exhaustive of explaining the petition signing phenomena. In qualitative research terms, we had reached a level of theoretical saturation [63]. We finalise the current state of our codebook and proceed with further steps of our study.

28 Figure 5.2: Bar indicating current signature count and signatures left to go on different platforms

Figure 5.3: Different petition platforms highlighting their recent signtories

Figure 5.4: Example of an artifact displaying solidarity from change.org

29 5.2 Findings

Figure 5.5: Comparative table of goals, and parallel features between two systems

In light of the codebook we built outlining the features of different platforms, we list out the user goals we can achieve with them and the affordance they provide. From our ﬁndings of previous chapter, we list down the user goals’ of ”Twitter/Appstore” boycotters and how they achieved them. We draw parallels between the two collective action settings - online petition platforms explicitly supporting such campaigns, and emergent online collective action and attempt to formalise the phenomena.

1. Visibility on current state of campaign: We observed how petition platforms would depict the number of petition signatures that were gathered at any moment. It would usually be an “indicator bar” indicating the number of signatures required, and number of signatures gathered so far (as shown in ﬁgure 5.1) . Research [29] informs us how petitions which pick up signatures early and build a momentum, reach their

30 stated goals faster. It is thus important to apprise the current state of the campaign to galvanise the support base. We see that the “Twitter/Appstore” boycott campaign utilise the Snapdeal appstore ratings as parallel to their ”indicator bar”. Additionally, “progress visibility strategy” in tweet framing, indicating real (or, unreal) progress on the ratings of Snapdeal, intends to achieve the same goal of galvanising the support base.

2. Medium to channel reaction into an action: On petitions platforms, this medium to channel reaction into an action would come in forms of call to action buttons which were prominently placed on the websites. A few platforms also had interactive elements which would take the user to a donation page for the said cause. We clearly know that in case of Twitter/Appstore boycotters, uninstalling, writing bad reviews, and downrating the application were the mediums to channel reaction to Aamir Khan’s statements. This was made possible due to affordances provided over Playstore which demand user review and rating for generating an aggregated score and ranking of the application on the Appstore. Along with this, the ability to “install” and “uninstall” an app can be a powerful statement to make over the companies’ policies. Unlike traditional boycotts and denying purchase, the impact is visible much faster to the company.

3. Sharing in your social network: As research demonstrates [1], social sharing of petition is correlated with it’s likelihood to get signatures. As we noted on our petition platforms, integrations with other social networking sites such as facebook, twitter, and email has made it really easy to share them with the click of a button and all the platforms we studied had them. Change.org even states “Sharing leads to way more signatures” on its website. From our ﬁndings on Twitter/Appstore boycott behaviour, we saw how retweets made up a signiﬁcant portion of all tweets and hashtags were used to build topical communities and inform of the “anti-Aamir Khan” sentiment in the network with piggybacking through opinionated hashtags. Both, sharing of petition on social media and emailing, and utilising retweets and hashtags to trend and inform people sympathetic to the cause seem to serve the same purpose and likely have the same effect.

4. Indicators of actors in power and who the grievance is directed at: We observed how petition platforms like avaaz.org and petitions.whitehouse.gov would explicitly have section mentioning who the petition is directed at, such as - petitioning the National Science Foundation for decreasing research grants. From our ﬁndings (from prior chapters) , we have seen how collective @-mentioning user behaviour on Twitter engages in establishment of target, and disseminates this information in the network. By appropriating the usual @-mentioning behaviour of engaging in conversion into collective targeting, the boycotters are engaging in an act which achieve the same purpose as website elements which indicate and mention who the petition is directed at.

31 5. Visibility of reasons of participation: We observed how the platforms let users mention their reasons of signing the petition through comments, and was supposed to be an action that accompanies signing the petition. Brauns- berger et al [12], conducted qualitative research to identify the intent of participation in Canadian seafood boycott which had grown popular in 2009. They found that comments reﬂect the intent of boycott very closely and are an important step in signing of the petition. Analogically, appstore reviews served this outlet in our campaign of study where users were found to express intent of boycotting Snapdeal with reviews such as “We are giving Snapdeal 1 star and also boycotting any purchase from you (sic), unless Aamir Khan is removed from your company. Goodbye”.

6. Artifacts which encourage solidarity: We noted elements on websites such as “Please sign the petition today!”, “Together we can bring change!”, “We the people ask the Federal Government to call on Congress to act on an issue”, and how websites like change.org contained a bottom sheet requesting solidarity with curated text such as ‘‘Today [Arshita] is counting on you, [petition] Please join [Arshita] and [10535] supporters today” (as shown in ﬁgure 5.4). We saw how solidarity tweeting strategy was utilised to reinforce how participants who felt strongly against Aamir Khan’s statement must engage in boycotting and stand in solidarity.

7. Display of personal alignment to the cause: Studies on collective action motivations for signing online petitions highlight the importance of personal identity alignment with the cause as it enhances people’s self esteem, and humanises the signatories of the petition [68][42]. We saw the use of list of recent signatories displayed on petitions (as show on ﬁgure-5.2), and even people’s comments on the petition next to their name as aligning to the cause. In the case of “Twitter/Appstore”, we saw users proudly associating with the campaign by using their personal handles and tweeting about it, as well as posting screenshots of them uninstalling or downrating the app.

5.3 Conclusion

Conducting a content analysis of online petition platforms, helped us analyse the features they are designed with and the user goals’ they let petitioners achieve. When we drew parallels with our online boycott campaigners’ and their practices, we were able to formalise the entire system. We argue that despite Twitter and Appstore not being explicitly designed for conducting collective action, become channels for conducive action by creative appropriation by participants. Thus, these practices form a system which as coherent as online petition systems, This despite the lack of explicit design and intention. Outlining the 7 components sourced from our ﬁndings, can be useful starting point for unpacking similar such phenomenon.

32 Chapter 6

Discussion

6.1 Moderation challenges due to emergent collective action

From our findings we see how participants of an online boycott campaign were able to utilise “framing” and “opportunity” structures of online platforms, to shut down alternate voices and shape narratives. As we unpacked large scale user behaviour and tweet framing techniques, we found how technological affordances get appropriated to achieve specific goals and how certain arguments are made on Twitter and tweets framed to engage and mobilise people to mark their protest. As we drew parallels with the online petition system and identified how features, affordances and user goals are shaped, we were able to systemise our emergent collective action phenomenon. This provides us with important insights on challenges in preventing acts of harassment, doxxing, misinformation, and coordinated campaigns that have become prevalent on online platforms [49][5][62]. From research in this area [25][64][49], and from our findings, we argue that given opportunity structures which appear in ways of technological affordances such as - “Medium to channel reaction into an action”, or “Visibility on current state of campaign” - that appear in many different ways on platforms - can get “weaponised” as ways of coordinated campaigns to troll, doxx, harass or marginalise. For instance, if there are opportunities to provide online reviews of a service, contact us forms, comments section, ratings, online polls, reporting a page or a profile, they are likely to get weaponised for such goals. If they provide aggregate, and moving counts on these features say - 5 new comments, all nega- tive - they are more likely to get weaponised as they providing real time count and also are a medium to register their dissatisfaction. Designers of these platforms thus need to be aware of how these features can get weaponised while designing them and may not reflect the true sentiment or the intended use through the feature. Delaying instant count can be helpful as it breaks the pattern of visibility of collective action. Systems can then be designed to detect if there are patterns of malicious activity, or en masse comments echoing a particular sentiment non-aligned to the intended purpose of the poll/review/similar feature. i If not taken care of, incidents like these may even deplete collective trust in these aggregated and crowdsourced information.

33 Research informs us how social media platforms have become networked sites of political culture and important for disseminating the progress of movements, posting updates and shaping frames. While research on reddit/4Chan has examined how political subreddits and communities mobilise their faction to a certain goal [9][25], we observe similar behaviour here, despite Twitter not designed explicitly to support communities unlike Reddit. We see how hashtags can be modes of carrying messages in the network as well as act as temporary topical communities. Tweets played a significant role in shaping early framing and positioning to users on this platform sympathetic to the cause, and then engaging them in rhetoric through strategic tweeting. This informs us that not only users on platforms built around sub-communities can partake in co-ordinated collective actions, but users can utilise aligned features on their respective platforms to align towards common goals. As a moderation challenge this can become difficult when, for instance, users of instagram are organising through creating pages targeting a particular individual and sharing co-ordinated stories to malign them. For designers of systems, it is thus important to be cognisant of the fact users are not merely aligning on what maybe the primary format for the platform (text or audio or video), but tangential features also present opportunities to building their network of influence and alignment.

We argue that given “opportunity” structures, such as, the facility to send and receive responses between sympathisers of a cause, ability to align socially with others on the internet - it will become easier over time for “trolls” or “louder voices” to shape discourse, or harass and marginalise individuals. Researchers in HCI might therefore ﬁnd value in understanding emergent phenomena or coordinated/collective campaigns through lens of the - affordance they are utilising, the goals’ they are achieving and the features and platforms they are using. Often times these might be very similar to the ones we found in our study. Designers of systems need to be aware of the fact that designing any feature where aggregated or crowd sourced opinion is being taken has the potential of being weaponised. Re- searchers employing trace ethnography [cite], a research methodology tool which trace and document activities over time and supplement them with “thick” descriptions can utilise our recommendations and frameworks to make sense of such events.

6.2 Opportunities in designing for collective action systems

While our ﬁndings highlight the challenges that can arise when it comes to moderation on these platforms that lead to marginalising and targeted campaigns against individuals/services online. we also present potential design learnings for building better online collective action systems. From analysing online petition websites, and formalising our emergent collective behaviour into the goals’ the users were able to achieve and what they were utilising to achieve those — we identify opportunities that can be utilised by designers of civic media systems. We saw how “Direct Calls to Action” strategy was retweeted by the most number of boycotters. These tweets which were framed to clearly outline the steps to take (go to app store – select app –

34 downvote/uninstall) were appropriated as a way to effectively challenge the narrative of “intolerance”. Thus, we see that being direct and providing a channel to act on the grievance is an effective way to get people to act. These results are in line with ﬁndings of Savage et al [64] where “direct calls” strategy by bots were more effective in engaging with potential activists for a cause online, as opposed to more “manipulative calls”. We also see from both petition websites, and our case study, ‘Visibility on current state of campaign” is an important feature to be integrated as it mitigates risk of wasted effort which is one of the challenge in executing collective action. We also saw how “Visibility of reasons of participation” was a common theme across all petition websites and the boycott campaign. There is hence value in designing for getting people to engage in ways that they like. There is also a recurring notion of addressing who the concerned authority for change is through an element indicating ‘‘Directed to” and people’s personal alignment to the cause with “Directed from”. Combining all these factors, effective online civic system design that employ value in collective action could be built with following in mind – 1) people are able to convey their struggles through text, video, or images 2) address it to a particular authority 3) Others are able to see momentum pick up it from them 4) attach their local credentials which are valued in the community 5) The calls to action are direct and build on solidarity.

Lately, apps such as Nextdoor 1 and Neighbourly 2 are becoming popular which help localise issues and concerns between residents of a neighbourhood and discuss amongst themselves. We saw how tem- poral communities were formed for effective collective action in our study. As these apps are centred around neighbourhoods and communities , they can be designed to support effective organising around civic issues amongst people. Along with this, Governments are often met with collective action challenges such as raising money for floods or national calamities. Our findings extend in these phenomenon to solve for mitigating risk of wasted effort, free rider problem and issues of outreach and engagement. A future campaign to raise funds could have central conveyors directed calls to action and friction-less donation, encouraged sharing of receipts of donation, regular updates of amount collected, and building an influential retweet network on platforms like Twitter.

1www.nextdoor.com 2 https://play.google.com/store/apps/details?id=nz.co.neighbourlyhl=enI N

35 Chapter 7

Conclusions and limitation

We present our ﬁndings and learnings on user goals, behaviour and platform affordance in a collective action setting through a case study of Snapdeal Boycott over Aamir Khan’s statement. In chapter3 we unpack the behavioural patterns observed on large scale through the lens of use of hashtags, retweets and mentioning. In chapter4 we utilise framing theory to make sense of arguments that were framed to mobilise users, and various strategies of tweeting to engage users into act of boycotting. In chapter5, we conduct a content analysis of design of 5 popular petition websites to understand the intended user goals, and affordance on these platforms and drew parallels with our online boycott campaign. This helped us formalise our ﬁndings into a system by drawing parallels, and identifying major affordances that help users achieve their collective goals’ in action. Once systemised through the lens of goals’ and affordances, we developed a better understanding and suggest design challenges and recommendation for moderation and building civic systems online in chapter6.

7.1 Methodology limitations

As a large part of our study utilises qualitative research methods and interpretive epistemology, we are limited in terms of generalisability, and predictability of phenomenas. Future work can build on providing more causal analysis between action and events that follow. There is also an opportunity in say, tracing out micro-patterns in retweet network and their impact on polarisation on the platform in context of boycott and collective actions. We could also build more detailed codebooks of framing devices used by boycotters to analyse them more closely, or ﬁnd recurring patterns between different boycott or similar campaigns. Despite this being a cross-platform study, we believe there are even more insights that can be gathered by increasing the platforms that we cover to include messaging platforms such as Whatsapp as points of co-ordination and message dissemination, popular social networking platforms such as Facebook and their groups and pages, online websites and how they report and cover these events. Interview-

36 ing participants of such campaigns can uncover their motivations, and challenges that they address in participating. Future work can address these limitations or extend our ﬁndings and insights.

7.2 Design as a socio-technical enterprise

We recognise that technology is political [79], and so are all technological choices. Our claims about mitigating platform appropriation and harm are hence not free from such choices, and their implications. They are intrinsically linked to discussions in the science, technology and society studies space on power dynamics [53][37], privilege [30], values of liberty [19], speech, censorship [77][21], and many different costs of being online (or not) [75]. From our position we state that designers adopt a more conscious approach on building affordance that promote positive collective behavior, and observe socio- technical phenomenon as larger systems that exist beyond the interface. This also links to us valuing reputational harm as a serious issue where signal of a property, such as quality of service, gets mired with socio-political connotations, and often fringe ones. This discussion is different from deplatforming where the boycott, or removal is directly related to the actions of the corporations (or people) involved with it. We take a normative position by stating that these signals must be delineated clearly to protect non-aligned reputational harm caused in a collective fashion. Many of our concerns rise beyond just design of these platforms and into laws and regulations as has been noted [41]. But, as technology inhibits or prompts certain value and behaviour, we believe a preemptive design thought could promote better socially mediated interactions. There is recent prece- dent, where Twitter made design changes on their platform to slow the spread of crucial election related misinformation 1 . While this can be handled on a law and norms related level, pro-active design helps mitigate certain issues too. As more products and services register an online presence, containment of malicious reputational harm must be a core function of the platform.

1https://www.bloomberg.com/news/articles/2020-11-12/twitter-says-changes-curbed-spread-of-election-misinformation

37 Related Publications

1. S. Prabhat, A. Motwani, N. Rangaswamy “Longitudinal analysis of a #boycott movement on Indian online platforms: Case of collective action and online boycott”, The 17th European Conference on Computer Supported Cooperative Work, ECSCW ’19

2. S. Prabhat, A. Motwani, I. Mangurkar, N. Rangaswamy “Framings in collective action: Case of online #boycott”, 25th Americas Conference on Information Systems, AMCIS‘19

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