How the Dogs-Of-Instagram Reveal Voter Preferences

Social Semiotics

ISSN: 1035-0330 (Print) 1470-1219 (Online) Journal homepage: https://www.tandfonline.com/loi/csos20

“Lucy says today she is a Labordoodle”: how the dogs-of-Instagram reveal voter preferences

Helen Caple

To cite this article: Helen Caple (2019) “Lucy says today she is a Labordoodle”: how the dogs-of-Instagram reveal voter preferences, Social Semiotics, 29:4, 427-447, DOI: 10.1080/10350330.2018.1443582 To link to this article: https://doi.org/10.1080/10350330.2018.1443582

Published online: 27 Feb 2018.

Submit your article to this journal

Article views: 304

View Crossmark data

Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=csos20 SOCIAL SEMIOTICS 2019, VOL. 29, NO. 4, 427–447 https://doi.org/10.1080/10350330.2018.1443582

“Lucy says today she is a Labordoodle”: how the dogs-of- Instagram reveal voter preferences Helen Caple School of the Arts and Media, Faculty of Arts & Social Sciences, The University of New South Wales, Sydney, NSW, Australia

ABSTRACT KEYWORDS Instagram is an image-centric social media application, launched in Australian federal election; October 2010 with the explicit aim of allowing members to share CAMDA; Instagram; their smart phone photos with the world. Posts typically combine multisemiotic analysis; voter photographs with a short verbal text, and as such provide fertile preferences ground for multisemiotic analysis. Further, at times of political significance or upheaval, Instagram, like other social media platforms, provides a space for the public to express opinions or emotions. The fact that citizens do this through a combination of words and images on Instagram is the subject of analysis in this article. Using a dataset of 92 Instagram posts that made use of the discourse tagging hashtag #dogsatpollingstations at the time of the 2016 Australian federal election, this study demonstrates the multisemiotic strategies used by members of the public to show their dissatisfaction with the incumbent government, and their endorsement of other political parties. The study also demonstrates the triangulation of multiple methods, and as such is an example of corpus-assisted multimodal discourse analysis.

Introduction The Australian federal election of 2 July 2016 was a double-dissolution election, meaning that all members of parliament (in the Senate and the House of Representatives) were up for re-election. The double-dissolution was triggered by the inability of the government to pass three bills: Building and Construction Industry (Improving Productivity) Bill 2013; Build- ing and Construction Industry (Consequential and Transitional Provisions) Bill 2013; and the Fair Work Amendment Bill 2014. In an attempt to resolve this impasse (i.e. by increasing his party’s majority), the incumbent (conservative) Prime Minister Malcolm Turnbull called an early election. The move proved disastrous, was roundly criticized in the media (Davidson 2016; Karp 2016; Leslie 2016; Murphy and Karp 2016), and almost ended in a hung parliament; and while the Liberal/National Coalition eventually won the election and remained in power, their majority was massively reduced (to one seat). The Liberals lost 14 seats; the Australian Labor Party gained 14 seats. Australia has compulsory voting, and many Australians see voting as rightful participation in a democratic process, a civic duty not to be taken lightly. The election was viewed as a

CONTACT Helen Caple [email protected] © 2018 Informa UK Limited, trading as Taylor & Francis Group 428 H. CAPLE rebuke by the Australian public against the conservative government (Kenny and Massola 2016), and one of the places where the public aired their grievances against the government was on social media (Caple 2018). The aim of this paper is to investigate public engagement with politicians and their parties/polices during the 2016 Australian federal election that emerged on the photo-sharing site, Instagram, as expressed through both verbal and visual semiotic resources.1 The dataset consists of 92 Instagram posts that made use of the discourse tagging hashtag #dogsatpollingstations, and which were posted on this social media site on 2 July 2016 (the day of the federal election). This is a “companion” dataset to a 6299-post corpus of Instagram posts that were collected over a 5-day period either side of the election, and which made use of the very common Australian election hashtag #ausvotes (Zappavigna 2014; Bruns and Burgess 2015). Quantitative analysis of the #ausvotes corpus is reported on in Caple (2018). The current paper focuses on qualitative multisemiotic analysis of this much smaller #dogsatpollingstations (hereafter DAPS) dataset, in order to answer the question of how Instagrammers (people who post content to Insta- gram), through their posts to Instagram, affiliated with or distanced themselves from politicians/political parties during the Australian federal election of 2016. The analysis triangulates corpus linguistic methods (examining word frequency and concordances) with multimodal discourse analysis (following Kress and van Leeuwen 2006; van Leeuwen 2008) and as such is an example of corpus-assisted multimodal discourse analysis (CAMDA) (Bednarek and Caple 2014, 151). The paper also draws on Zappa- vigna’s(2015) work on the linguistic functions of the hashtag – metadata that is commonly used in social media writing as a form of discourse tagging and searchable talk (Zappa- vigna 2011, 789). Before exploring these methods in more detail, however, I provide a brief summary of research examining social media, in particular Instagram, and how it is used by the public to express their ideas and opinions on key political issues, e.g. elections.

Social media, opinion and politics Social media stands at the forefront of much of what we do online: be it to connect with friends and family (e.g. via Facebook), to broadcast short messages to the world with the hope that our messages are of interest and use to someone (via Twitter), or to share photos and videos with the world (via Instagram). This is taking place on an enormous scale: Instagram alone has more than 800 million members (About Us 2017), and more than 52 million images are published via this application every day (valid August 2017). Researchers from a wide range of disciplines are interested in exploring the connections that are made via social media and this has created a burgeoning research field in a number of areas: how societies are networked through social media (e.g. see Papacharissi’s 2010 edited volume on networked publics; Hochman and Manovich 2013; Highfield and Leaver 2015); how communities are formed through bonding over similar emotional responses to phenomena (Zappavigna 2012; Bruns and Burgess 2015); and how vitriol, bul- lying and hate speech are spread throughout these social networks (Jane 2014a, 2014b, 2017) to name but a few of these. Scholars have long demonstrated the function of social media as a means of dissemi- nating information (Small 2011). Indeed, Twitter often beats the legacy media in the break- ing of news (Jewitt 2009, 234). However, social media can also be viewed as a barometer of public opinion on virtually any issue (e.g. Kreiss [2016] on the 2012 US electoral cycle). SOCIAL SEMIOTICS 429

Writing about blogs, Davis (2009, 35, bold in original) posits that “the blogosphere was a highly democratic and inclusive medium that reflected the public’s opinion rather than that of elites”. Coleman and Blumler (2009, 87) label blogs as: “sophisticated listening posts of modern democracy”. Kreiss (2015, 2) further endorses the point that social media platforms “provide a new socio-technical means of producing and representing public opinion”, and suggests that “political staffers feel obligated to monitor social media sentiment around events and take it seriously enough to spend weeks planning how to influence it”. Similar findings can be observed in linguistic studies of social media. Zappavigna (2012, 173) asserts that microblogging (Twitter) allows researchers access to “a veritable treasure trove of linguistic data about public thought, unprompted and spontaneous opinion”.In particular, at times of political significance or upheaval, citizens will use social media “to voice a political opinion or present an emotional response” (Zappavigna 2012, 173). Indeed, much of Zappavigna’s(2011, 2012, 2014, 2015) research on the microblogging platform Twitter is situated in the realm of the interpersonal and she asserts that “it is interpersonal meaning that builds and sustains online social networks” (Zappavigna 2012, 11). The focus of much of the research examining political discourse on social media has to date focused mainly on how politicians use social media to campaign during an election and how the news media cover that activity (Gibson, Lusoli, and Ward 2008; Grant, Moon, and Busby Grant 2010; Bruns and Burgess 2011; Macnamara 2011; Small 2011; Broersma and Graham 2012; Burgess and Bruns 2012; Larsson and Moe 2012; Bruns and Highfield 2013; Kreiss 2016; McGregor and Mourão 2016). There is less research investigating how the public express their ideas and opinions on politics and elections through their social media use (cf Gibson and Cantijoch 2011; Zappavigna 2011; Caple 2018; Burgess and Bruns [2012] on the Twitter response to political campaigning; Velasquez and Rojas [2017] on the expression of political ideas more generally). Even more lacking is semiotic research that demonstrates how these ideas are expressed in both verbal and visual semiotic resources working together to create meaning, in what Kress (2010, 28) terms the “modal ensemble”. This paper addresses this shortfall by engaging in semiotic analysis of the verbal and visual resources that were deployed in Instagram posts made by members of the public on the day of the Australian federal election in 2016. It is also a contribution to the growing body of work that investigates how words and images are struc- tured on social media platforms, in this case on Instagram, and the extent to which this may or may not contribute to discussions on key political issues of the day.

Analysing the Instagram post The Instagram post, as a modal ensemble, usually consists of an image and caption. I use the term image here as a cover term for the visual component of the post. This is commonly a photograph, but may also be a video, cartoon, diagram etc. I use the term caption to refer to any text that is placed in the space below or to the side of the image, and that is included in the original Instagram post. This “text” may be made up of metadata only (lists of hashtags, or @user mentions, used to tag a particular social media user), e.g. #goldendoodle #groodle #timhammond #labor #dogsofpollingstations #election #dogsatpollingstations. It may consist of verbal text ranging from the minimalistic initialism commonly found in social media language, e.g. OMG, through nominal groups 430 H. CAPLE and fragments, e.g. “Election day”, “Boris thinking about his vote”, to clauses and sen- tences, e.g. “We voted”, “We looked after lil Molly while her person voted today!”;orit may be a combination of both verbal text and hashtags. In some cases the hashtag may be integrated into the linguistic structure of the verbal text, e.g. “#election day” or “Disappointed doggies there was NO #election2016 #DemocracySausage #SausageSiz- zle where we do our #ausvotes ”.2 Figure 1 shows an example Instagram post from the DAPS dataset. Since the Instagram post is a multisemiotic text (combining image and language), this research draws on a number of approaches to multisemiotic analysis to interrogate how images and words contribute to meaning making. Even though this dataset is relatively small (1438 words, 92 images), the analysis starts from a “text-as-corpus” perspective, which reveals textual patterning and recurrent meanings across the whole verbal data (Bednarek and Caple 2017, 21), and which may assist in determining the focus of the texts (Baker 2006, 71). Therefore, corpus linguistic methods are used to generate word lists (showing the most frequent words, see Table 7), and concordance lines (showing a word in context, see Figure 3), using AntConc (Anthony 2014), in order to discover whether there is a wide variety in the topics discussed in the verbal component of this dataset, or whether the focus remains on one topic (e.g. the election). This approach is complemented by a “text-as-text” perspective, examining the discourse semantic structure of each caption in turn for how Instagrammers express allegiance to a particular party. In examining the expression of voter preferences, I draw on van Leeuwen’s(2008, 28) notion

Figure 1. An example Instagram post (as viewed on a computer screen). SOCIAL SEMIOTICS 431 of “exclusion” from his social actor network to examine what is present in or absent from this dataset. As van Leeuwen (2008, 28) notes in relation to verbal exclusion: “representations include or exclude social actors to suit their interests and purposes in relation to the readers for whom they are intended”. In this dataset, voting preferences are revealed through the inclusion of certain policy issues (#equality, #savemedicare) and not others, and in the naming of certain politicians (#timhammond), political parties (#alp) or electorates (#barton) and not others. Analysis of the visual component of the Instagram post includes analysis of each image for representational meaning: who or what is the subject matter of the images (represented participants), what is happening in the image (activity sequence) and where this is taking place (setting) (Kress and van Leeuwen 2006, 48). It also includes examination of the visual attributes of the image: clothing, realia and other objects that may constitute the parts of the represented participants or the setting (Kress and van Leeuwen 2006,50).Doingsoallowsfor further scrutiny of these visual attributes through the lens of van Leeuwen’s(2008,147) visual social actor network. As with the verbal analysis, such an approach may be used to show whether certain representations and attributes are excluded from this data and thus whether this points to allegiances with particular political parties and not others.

The DAPS dataset The data examined in this paper is a small companion dataset to a larger case study investigating news reporting and social media use around the Australian federal election of 2016 (see Footnote 1; Table 1). The dataset consists of 92 Instagram posts that made use of the discourse tagging hashtag #dogsatpollingstations, and which were posted on Instagram on 2 July 2016 (the day of the federal election). Another dataset using the hashtag #democracysausage was also collected on the day of the election, as this hashtag, like #dogsatpollingstations, was trending on the day of the election.3 The make-up of the final data collected from Instagram is given in Table 1. Since this paper focuses on qualitative multisemiotic analysis, I report here only on the analysis of the much smaller DAPS dataset. Motivation for focusing on the DAPS dataset for investigating the expression of voter preferences comes from the initial analysis of the frequency of certain words in each of these datasets in Table 1. By examining the top 100 most frequent words for each of these datasets, it became clear that posts using #dogsatpollingstations are much more likely to include reference to a political party than posts in either of the other datasets (see Table 2). In fact, the overuse of mentions of the Labor party is statistically significant (Log-likelihood [LL] = 15.76, p < 0.0001) when comparing the DAPS dataset with the #ausvotes corpus. Likewise, the overuse of mentions of the Greens is also statistically significant

Table 1. Instagram data collection for case study on 2016 Australian federal election. Hashtag Total No. of posts collected ausvotes 6299 democracysausage 927 dogsatpollingstations 92 432 H. CAPLE

Table 2. The normalized frequency of named political parties ranked in Top 100. Dataset Party Raw Tokens Frequency Normalized Freq. ausvotes Greens 421 132909 0.003168 3.17 Labor 308 132909 0.002317 2.32 DemocracySausage Greens 29 16108 0.0018 1.80 DAPS Labor 13 1438 0.00904 9.04 Greens 9 1438 0.006259 6.26

(LL = 8.38, p < 0.01) when comparing the DAPS dataset with the #DemocracySausage dataset. Indeed, as the case study presented in this paper will show, 50% of the posts using #dogsatpollingstations reveal voting preferences. However, this preference is not always directed at legitimate political parties, as some dogs have aspirations of high office for themselves, e.g. “Sooty for PM” or their breed, e.g. “#voteforpugparty”. As Table 2 shows, dogs and their human overlords are highly likely to mention political parties. Whether these mentions reveal political affiliation is the subject of the qualitative analysis that follows. But first, the general make-up of the DAPS dataset is outlined in the next section.

Findings: general make-up of the DAPS dataset To give an overview of the DAPS dataset, Tables 3–6 summarize key findings from the data collection process and analysis of voter preferences. As shown in Table 3, by and large users only made one post using #dogsatpollingstations on the day of the election, with 77 unique users. Only one person posted more than twice to the hashtag, making 6 posts, each time showing a different dog sitting outside a polling station. The highest number of “likes” received by a post reached 387, and no post received less than 3 “likes”. Table 3 also indicates that voter preferences are revealed in a total of 46 posts (50%) in the DAPS dataset. These preference are predominantly left-leaning (in 67% of posts), or favor invented parties (22% mainly for dogs e.g. “Pug Party” and sausages, “Sausage Sizzle Party”,or“Democracy Sausage Party”). The Liberal Party, which went on to win the election, attracts the most dissent and only marginal support. The reasons for this ambivalence towards the Liberal Party will be discussed in the final section. As shown in Table 4, the caption is largely made up of a combination of verbal text and metadata (hashtags), rather than only hashtags, which means that the majority of posts did have something to say and were not simply categorizing the post (although, as the analysis

Table 3. Summary of findings from the DAPS dataset. Category Number Total number of posts using #dogsatpollingstations (on 2 July 2016) 92 Number of unique contributors: 77 Highest number of individual user posts to this hashtag 6 Highest number of likes received by one post 387 Accounts owned by dogs 22 Posts expressing voter preference 46 Posts that are left-leaning 31 Posts that are right-leaning 3 Posts supporting invented parties 10 Posts distancing themselves from a party 7 SOCIAL SEMIOTICS 433

Table 4. Caption type. Type Number % Example text: Verbal text + metadata (hashtag) 79 86 It takes all sorts #election2016 #dogsatpollingstations #democracysausage Metadata only 13 14 #dogsatpollingstations #ausvotes #sharpei

Table 5. Image type. Type Number Photograph 90 Meme 1 Montage 1

Table 6. Where voter preferences are expressed in the DAPS dataset. Where realized Number % Image only 8 17 Image and Verbal Text 2 4 Image and Hashtag 12 26 In all 3 5 11 Verbal Text only 8 17 Verbal Text and Hashtag 5 11 Hashtag only 6 13 below shows, hashtags are also used to express an opinion). The photographs that make up this dataset are conventionally produced (see Table 5) and only one meme and one montage is used, and are shown in Figure 2. Memes include formulaic language (and inter- textual references) often with typographic devices such as hashtags and image-text combinations, and which are deployed for social bonding rather than for sharing information (Lankshear and Knobel 2006, 101). The meme used in this dataset draws on the negativity that was circulating on social media at the time of the election towards a campaign adver- tisement made by the (conservative) Liberal Party about negative gearing. The advertise- ment was poorly produced and contained a number of inaccuracies in relation to building site regulations to the extent that the “tradie” (tradesperson) animating the adver- tisement was accused of being fake, when in fact he was real (Mitchell and McIlroy 2016). As a result, there were a number of “fake tradie” memes making fun of this man and the conservative values he represented circulating on social media at the time of the election. The meme posted to #dogsatpollingstations depicts its own “fake tradie”: a dog wearing a high viz vest standing in the back of a Ute (a type of pickup truck), the vest and the Ute being two common attributes of the Australian “tradie” (Example A, Figure 2). The verbal text superimposed onto the image reads: THIS FAKE TRADIE RECKONS WE SHOULD PUT THE LIBS LAST! and as such is an instance of political distancing (from the Liberal Party), by advising on the ordering of preferences on the ballot paper. Only one post is a montage and is made up of four images (Example B, Figure 2). It sums up the whole election experience by representing four key aspects of the day: Parliament House in Canberra (the seat of government that is being decided on this day), the democracy sausage, a dog at a polling station, and a town hall polling station with political party campaigners (town halls and schools are typical venues for the voting process to be carried out in). This is further summed up in the caption text: “The election experience”. 434 H. CAPLE

Figure 2. Examples of a meme (A) and a montage (B).

Finally, Table 6 shows where in the post (image, hashtag or verbal text or in any combination of these) voter preferences are expressed. It appears that hashtags and images are the most likely elements of the Instagram post to be used to express preference for a political party. In the following sections, I will first examine the extent to which the linguistic component of the Instagram post is “on topic” (i.e. talking about the federal election, dogs at polling stations or dogs in general) and then look at where and how voter preferences are linguistically realized. Then, I examine the contribution of images both to the semantic domain of the federal election and to the revelation of voter preferences. The order of this analysis is not meant to show preference to the linguistic analysis over the visual analysis; rather it allows for a logical progression that culminates in bringing the two sets of analyses together to look at the post as a modal ensemble, and not just broken down into its constituent parts.

Interrogating the linguistic component of the Instagram post As noted above, the linguistic analysis sets out from a “text-as-corpus” perspective, reading the texts vertically, for formal patterns and for repeated events, before moving onto a closer horizontal reading of the “text-as-text”, and reading for content (Bednarek and Caple 2017), thus revealing how voter preferences are linguistically expressed. As noted above (and in Table 4), most posts make use of a combination of verbal text plus hashtags (86%), with the hashtags either listed together (as in the example shown in Table 4) or integrated into the grammar of the clause as in “Braving the Canberra cold for the coveted #democracysausage”. 14% of posts use only hashtags. In fact, hashtags are used a total of 566 times throughout this dataset, giving an average of 6 hashtags per post. The fact that hashtags may operate within the linguistic structure of the verbal text or separately as metadata presented methodological issues both for compiling word lists and for the impact this might have when examining the frequency of recurring words. Given SOCIAL SEMIOTICS 435

Table 7. Frequency list of word forms in the DAPS dataset. RANK Frequency Word form 1 45 emoji 2 36 #ausvotes 3 29 the 426to 5 20 #election 6 19 vote 7 18 #democracysausage, #dogsofinstagram 8 17 for 9 15 #auspol, a, election 10 13 is 11 10 and, day, today, you 12 9 i, s, she 13 8 #dog, but, t, that, we 14 7 democracy, her, in, sausage, with 15 6 abcnews, at, have, on, our, this 16 5 #democracydog, #labor, #vote, be, if, my, voting 17 4 #australia, #dogsatpollingplaces, #dogsfordemocracy, #dogsofaustralia, #greens, happy, he, no, polling, so, time, voted, was the very small size of this dataset (1438 tokens), it was possible to test different methods;4 however, there were in fact very few differences in frequency positions. The word list presented in Table 7 was generated in AntConc, with the # symbol as a User-Defined Token Definition and a minimum frequency of 4 occurrences of each word form. It clearly shows that the topic of conversation in the linguistic component of the DAPS dataset was firmly focused on the federal election. The use of emoticons in Instagram posts is highly frequent, appearing either first or second in frequency lists across all data sets captured in relation to the federal election (see Footnote 2). Lexical word forms such as election, vote, democracy and polling suggest that the focus of conversation in these posts is on participating in this process. The unique democracy sausage is also frequent, as are #dogs*, reminding us of another key topic in this dataset. Two political parties (Labor and the Greens) are also mentioned, again as hashtags. Both #ausvotes and the more general #auspol are highly frequent in this dataset and reflect a common practice among social media users who include “multiple potential hashtags in their message in order to ensure that it is visible to the largest possible audience” (Bruns and Burgess 2015, 22). Qualitative examination of concordances (showing words in context) of the most frequent non-lexical items (the, to, for, a) add further evidence to the fact that this is a

Figure 3. Concordances for the non-lexical item a. 436 H. CAPLE very cohesive dataset and that the topic remains focused on the election and voting. Figure 3 shows the concordance lines for all 15 instances of a. Again the democracy sausage is prominent (in 5 lines), and all of the instances of a are related to the election. In three lines (6, 10 and 13), it is not immediately apparent that the posts are focused on the election, although expanding the co-text shows that they indeed are:

(6) Making a friend while @busybusybusyp earns his democracy sausage. (10) Barry enjoying a pat in the line to vote (13) According to Poss, Australia could be doomed if today’s election doesn’t go the way she wants it to. The language might be a *tad* dramatic, but I love that she takes our democratic rights so seriously. Vote well people.

The concordance lines in Figure 3 also point to the ways in which the linguistic component of the Instagram post reveals voter preferences, ways that cannot be gleaned from the frequency list in Table 7. Lines 8 and 9 show how Instagrammers both distance themselves from (8: I’m not a Labor voter), and align themselves with (9: Lucy says today she is a Labordoodle) one political party. By shifting perspective to text-as-text (Bednarek and Caple 2017), we can look at the ways in which voter preferences are linguistically realized in each of the captions in turn. Here we start to see much greater variety in the kinds of parties (invented or legitimate) that these posts affiliate with and in whose name (dogs or humans) preferences are stated. In all there are 20 caption texts that express voter preference (see Table 6). A small number of posts do this through the use of commands (5) or exclamations (1) as in: “Vote 1 Ernie [a dog]”,or“Go the #greens”. Voter preference is also expressed through statements (12), often making use of either the verb or noun form of “vote”: “Millie’s voting for marriage equality and safe schools today”, “I’m not a Labor voter” or “we put #familyfirstlast”. In the last example, we can also see the inclusion of the hashtag as part of the clause, and the creation of wordplay between the name of the party “Family First” and verb + object complement “to put X last”. Another example of wordplay references the Labor leader Bill Shorten and plays with the sound similarity between the name of the dog (Bowie) and the name of the Labor leader (Bill Shorten). The caption text reads: “This is Bowie but today you can call him Bow Shorten!” Only two captions express emotion/desire (bold font) and one makes a very harsh judg- ment (underlined) of the incumbent government, which is also metaphorical (comparing the government to a piglet and taxpayers to its mother):

(1) #billygraves wasn’t too happy tied up next to #malcolm this morning and had plenty to say when I was inside #voting . I think he would have preferred to be next to the Animal RIghts Party sign (2) She really wants me to vote for these guys (3) The government is a greedy piglet that suckles on a taxpayer’s teat until they have sore, chapped nipples

Two further captions make use of reported speech, in which a human author is present- ing the “speech” of the dog that is photographed. The captions read: “Cruisy says vote SOCIAL SEMIOTICS 437 greens!” and “Lucy says today she is a Labordoodle.” The latter is also an example of wordplay based on sound/syllable similarity. Here the play is produced in the substitution of the dog breed “Labra-doodle” (a Labrador/Poodle cross in Australian English) with the name of a political party to give “Labor-doodle”. To sum up the analysis of the verbal text component of the caption, a corpus approach reveals the cohesiveness of this dataset in remaining focused on the federal election. However, closer inspection of each text in turn reveals how this group of Instagrammers express their opinions about the election, political parties and candidates. These are the opinion of both humans and dogs, and sometimes these opinions are voiced by the dogs themselves, e.g. Kirby the dog says: “#vote1 for Kirby and dog friendly polling locations. Had to wait for mum outside while she did her civic duty” (see Caple in progress for discussion of voice and authorship in Instagram posts). Turning now to closer inspection of the 566 hashtags that were used in this dataset, we can see a similar pattern of topic formation and affiliating strategies being deployed. Table 8 shows that hashtags are not only topic markers, they can be used to create humor, and can also draw attention to key issues (policy issues, voting preferences) around which Instagrammers can bond with each other. Table 8 also highlights the com- peting interests that are at stake in this dataset, since this group of users also convene around their pet guardianship and love of certain dog breeds. As Table 8 shows, hashtags draw attention to the event (#australianfederalelection) and the duty of citizens to participate in the democratic process (#everyvotecounts). This participation in the democratic process is also extended to dogs, through hashtaggery: the “irreverent and ritualized applications of the hashtag” (Highfield 2015, 2). As Highfield (2015, 2) notes: through hashtag word play, subversion, resistance and irony, social

Table 8. Examples of Hashtags used in this dataset. Topic Drawing attention to: Examples from the data Politics The event #australianelection; #generalelection; #australianfederalelection Power of the process #australiavotes; #australiadecides; #everyvotecounts Policy issues #equality; #health; #savemedicare; #animalrights; #discrimination; #earthdoesnotbelongtoman; #icycleivote Parties, politicians and their electorates #australiancyclistsparty; #greens; #alp; #timhammond; #barton Voting preferences #ivotedlabor; #votegreen; #familyfirstlast; #fuckyouturnbull; #givethelibslastpref Dogs and Power of the process (incl. word play) #dogsfordemocracy; #dogvotes; #dogmocracy; politics #dogocracy; #democrapup Policy issues #keepauspetfriendly; #dogfriendlycanberra; #dogsfordemocracysausages Voting preferences (legit) #dogsforlabor; #dogsoflabor Voting preferences (dog parties) #pugforpm; #voteforpugparty Dog breeds Specific breeds #cavapoo; #cavoodle; #cockerspaniel; #bordercollie; #goldendoodle; #groodle; #labrador; #labradorretriever; #australianshepherd Dogs and Sense of place in the Instagram world (through #dogsofinstagram; #dogstagram; #dogsofinstaworld; Instagram generic or breed-specific references: using X #petsofinstagram of Insta- gram, world conventions) #cavoodlesofinstagram; #bordercolliesofinstagram; #bullmastiffofinstagram; #cavapoosofinstagram Location Sense of place in Australia (playing with the X of #dogsofaustralia; #dogsofmelbourne; #dogsoftasmania; Instagram convention) #dogsofcanberra; #dogsofadelaide; #dogsofbrisbane 438 H. CAPLE media users can be both “flippant and concise”, “topical and irreverent” all at the same time. Thus, in this dataset, even dogs immerse themselves in the democratic process by exploiting the discursive flexibility of the language (e.g. #democrapup, #dogmocracy). Like with the verbal text discussed above, voter preferences are also overtly expressed through complex hashtag formulations such as commands “#givethelibslastpref”, statements “#ivotedlabor”, in the nominal group “#dogsforlabor” and through the use of exple- tives “#fuckyouturnbull”. These are examples of what Zappavigna (2015, 277) calls “meta- evaluation used to emphasize a particular political perspective”. Further examination of the hashtags using the notion of “exclusion” from van Leeuwen’s(2008, 28) social actor network also reveals voting preferences, as Instagrammers choose to focus only on certain policy issues (#equality, #savemedicare) and not others, and name certain politicians (#timhammond), political parties (#alp) or electorates (#barton) and not others. Listing all possible permutations of one party in a post also leaves audiences in no doubt as to where political allegiances lie, as in “#malcolmturnbull #turnball #lnp #liberal #liberalparty #coalition”. The revelation of voter preferences through these various formations of the hashtag demonstrates another function of the hashtag as a means of bonding around certain issues. As Zappavigna (2012, 85) notes: hashtag use “presupposes a virtual community of interested listeners who may or may not align with the values expressed together with the tag”. She further suggests that “putting the evaluation inside the hashtag flags the universality of the complaint/feeling, as it suggests that others share the same experience” (Zappavigna 2012, 93). Table 8 also reveals other ways in which Instagrammers bond with each other. These include through specific dog breeds, but also more generally as belonging to the world according to Instagram, for example through the conventional use of #[dogbreed]ofInstagram. Playful deviations from this convention can be seen in the final row of Table 7 where a sense of place in the Australian community is expressed through the use of #DogsOf[placename]. To sum up this section, the linguistic component of the Instagram post uses a variety of ways to both indicate and reinforce the (verbal) semantic domain of the post, to signal affiliation or disaffiliation with a particular topic, person or issue, and to indicate the community of Instagram users that are making these posts (dogs and their owners). The hashtag #dogsatpollingstations is also a very good indicator of the visual semantic domain of this dataset as the analysis that follows will demonstrate.

Interrogating the visual component of the Instagram post If one reads the hashtag #dogsatpollingstations literally, it refers to the co-location of a specific animal (dogs) with a specific location (polling stations), and polling stations were only operating on the day of the election. One of the key features of Instagram is near real-time posting (Zappavigna 2016, 272). The devices commonly used to create Instagram posts are mobile/smart technologies that have continuous web connectivity. Thus, the apprenticed Instagrammer is likely to capture an image, perform any manipu- lation of the image, add verbal text and metadata and post to Instagram all within a very short timeframe, and possibly while still at the location where the image was captured. As Zappavigna (2016, 272) notes, visual sharing on Instagram “can approach syn- chronous time”. Thus, Instagram users were most likely to post to this hashtag during SOCIAL SEMIOTICS 439 the time that they were actually at the polling station and voting (on 2 July 2016). This is another reason why this hashtag was trending on Instagram only on the day of the election. That this hashtag performs its function as a visual topic marker well is evidenced in the analysis of representational meaning in the visuals used in the DAPS dataset. Represen- tational analysis of the images asks the question: what are the images about? (Kress and van Leeuwen 2006, 48). The subject matter of the images overwhelmingly aligns with #dogsatpollingstations, in that the vast majority of images depict dogs tied up/waiting outside polling stations (Example A, Figure 4). 96 per cent of all images have dogs as subject matter, and 90 per cent of all images are set either inside or outside a polling station. Four images are potential outliers in this dataset, since they do not depict either dogs or polling stations. One “disruptive” post (Example B, Figure 4) uses a photograph of the inside of a football stadium showing spectators and players with the caption: Kick it through the big sticks ya muttt! #dogsatpollingstations #nicnatforpm. Here, the hashtag #nicnatforpm is the only potential connection to the election (it refers to Nic Naitanui, a football player). Otherwise Instagram users following the #dogsatpollingstations hashtag would find this post distracting and confusing. Another post that could be classified as “subversive” shows a cat with a human and uses the hashtags: #CatsAreTheNewDogs #trendsetter along with #dogsatpollingstations and #ausvotes. This post is could be viewed as aiming to rede- fine or subvert the remit of the hashtag #dogsatpollingstations. Such results suggest that there could be a range of user/post types that either comply with the remit of the hashtag, that disrupt the hashtag (providing largely irrelevant content), or that subvert the hashtag (usually in a humorous way) and provide an alternative meaning for the hashtag. Examples of each are provided in Figure 4. While the images in this dataset overwhelmingly comply with the topic marking remit of the hashtag under which they were collected, they do a lot more than merely depict dogs at polling stations. They also reveal voter preferences, as is evidenced in the discussion of further image analysis in the next section.

Imaging voter preferences By looking at photographs through a classificatory lens, as analytical structures in Kress and van Leeuwen’s(2006, 50) terms, one is interested not so much in what the participants are doing, but rather what they look like, what they are wearing, holding, or are near to. Here we shift perspective: participants’ roles become that of “Carrier” and by looking at their parts or “Possessive Attributes” that make up the whole, we can start to see that the combinations of meanings in the images in the DAPS dataset point to political affiliation. In seven images, dogs are depicted either wearing the official campaign t-shirt and/or badge for a political party (otherwise worn by human canvassers at polling stations), or are wearing a lead/collar, or a coat of the same official party color. 19 images depict dogs sitting in front of corflute signs, posters, or banners depicting one party and not another. In five images, paraphernalia such as how to vote sheets are held in front of or next to the dog. One could reasonably assume from such associations that the dogs in these images, as anthropomorphized non-humans, are showing support for one political party over another. Interestingly, political distancing is only realized verbally in this 440 H. CAPLE

Figure 4. Posts that comply with, disrupt or subvert the remit of #dogsatpollingstations. SOCIAL SEMIOTICS 441 dataset, either in the caption text and hashtags, or superimposed onto an image as in the fake tradie meme (discussed earlier and in Figure 2). In taking a more critical approach to representation analysis (but not necessarily in the service of uncovering “othering” practices), one can re-examine the depictions of represented participants in these images through the lens of van Leeuwen’s(2008, 147) visual social actor network. Strategies for interpreting the visual representation of image participants include: exclusion, (the limiting of) agency, homogenization and cultural and biological categorization. Such analysis gives further evidence of the function of hashtags as topic markers and the humorous subversion of this function. It also points to ways in which these images affiliate with or distance from particular political parties. The category of exclusion in visuals concerns “the possibility of not including specific people or kinds of people in representations of the group … to which they belong” (van Leeuwen 2008, 142). Since the dataset under investigation here has used a very specific set of parameters for data capture (namely that all posts make use of the hashtag #dogsatpollingstations and were published on 2 July 2016, the day of the federal election), analysis of exclusion would be helpful in providing further evidence of extent to which the hashtag functions as a topic marker. As already noted, dogs make up the overwhelming majority (96%) of represented participants, such that all other species are excluded from the dataset. As such, this action of exclusion further emphasizes the subversive nature of the single post that focuses on a cat and the bold challenge posed in the use of the hashtag #CatsAreTheNewDogs. Of the other three images that do not include a dog or a polling station, one is categorized as a disruptor (see Figure 4) and may be considered irrelevant. The other two images depict a different kind of dog – the hotdog – or as it is known on Election Day in Australia: the democracy sausage. These two posts may be interpreted as playing with the meaning of #’dogs’atpollingsta- tions, and as such may also be viewed as humorous subversive posts. Another element that is clearly excluded from this dataset is the vast majority of political parties. Of the more than fifty political parties (including independent candidates) that contested the Australian federal election in July 2016, only four were visually depicted in the posts using #dogsatpollingstations (see Table 9). A total of 22 images include paraphernalia associated with the Greens and Labor parties, one image includes a poster from the Animal Justice Party, and two images include Liberal Party corflute signs. One further image, the meme discussed above, includes verbal text that clearly distances the post from the Liberal Party.

Table 9. The political parties that voter preferences are directed towards and through which means. Breakdown of instances per element Total posts Image Hashtag Verbal text Political Party Affiliating Distancing Affil Dist Affil Dist Affil Dist Labor 15 1 12 11 3 1 Greens 13 – 12 7 3 Invented parties (dogs/sausages) 10 – 67 Liberal 3 5 3 1 1 2 2 Animal Justice 2 – 112 Family First – 111 Marriage Equality 1 – 11 442 H. CAPLE

With respect to “roles”, van Leeuwen (2008, 142) suggests that “people in pictures may be depicted as involved in some action or not”, and where they are involved in action they can be depicted as the “agents” (the doer of the action) or as the “patients” (the done to). The dogs depicted in this dataset are largely not involved in action, as they sit and wait for their owners outside polling stations. However as argued above, and as anthropomorphized non-humans, photographs showing dogs wearing campaign t-shirts, holding how-to-vote papers in their mouths, or sitting in front of campaign posters, may be viewed as somewhat agentive, encouraging us to vote, often in a particular way (see exclusion above). If we add in the interpersonal dimension of gaze, we can see that the agentive role of the dogs is further reinforced as they also engage with audiences through direct eye contact (in 8 images), in what Kress and van Leeuwen (2006, 118) call a “demand” relationship. To bring the analysis of voter preferences in the DAPS dataset together, Table 9 demonstrates which political party voter preference is directed towards and through which element of the Instagram post. Here we can see that it is in the images (28 times) and hashtags (27 times) that political affiliation is most likely to be expressed, while distancing from a party is only construed verbally. Support for invented dog/sausage parties is also realized verbally rather than visually. While Table 9 tells us how voter preference is realized across a range of semiotic modes, it does not say anything about the fact that words and images also jointly construct meaning as a modal ensemble. Kress (2010, 28) uses the term “modal ensemble” to

Figure 5. The Instagram post as a modal ensemble. SOCIAL SEMIOTICS 443 capture the fact that a communicative event/artefact is the result of the process of “assem- bling/organizing/designing a plurality of signs in different modes into a particular configur- ation to form a coherent arrangement” (Kress 2010, 162, italics in original). Thus, an alternative approach to the analysis of the Instagram post would be to engage with the whole text in its multimodal richness and as a coherent arrangement. This would allow the researcher to see additional configurations that construe voter preference that are not immediately obvious by examining each element in isolation, as exemplified in Figure 5. In the post in Figure 5, the viewer needs to engage with the whole post, in the Instagram environment (i.e. also looking at who the account holder is), to fully disambiguate the meanings of “she”, “me” and “these guys” in the verbal text. Thus, it is in the combination of the co- location of the dog with the Animal Justice Party poster and the invited direct eye contact between the dog and the viewer/account holder that we can fully appreciate the multiple ways in which this post aligns with the Animal Justice Party, and the extent to which the dog plays an active role in this exchange. This is, of course, exactly how a person browsing Insta- gram would see this post. For the researcher, however, it is a matter of acknowledging the limitations of the approach taken at the time of analysis (to analyse elements separately or as a modal ensemble from the outset), and what is missed in that analysis (that meanings may accumulate across modes), and to address any shortfall in subsequent research, as I do in relation this dataset in Caple (in progress). However, by analysing elements separately we can gain valuable insights into the contributions of each semiotic mode in turn to the construction of ideas, opinions or preferences. Returning to the results in Table 9, this analysis has shown that the Australian Labor Party and the Greens received the most positive support from people posting to #dogsatpollingstations, while the Liberal Party was mostly negatively viewed. There was also a fair display of irreverence towards the election, with invented dog and sausage parties receiv- ing a much larger vote of confidence than the incumbent government did (10 to 3). These are probably reasonable reflections of the way the 2016 election results played out, as will be discussed in the final section.

Conclusion The study presented in this article is a demonstration of the strengths and weaknesses to be found in the triangulation of corpus linguistic methods with multimodal discourse analysis. As an example of CAMDA, the study exposes the partiality of a corpus-only approach. Voter preferences were expressed in both words and images in this dataset, and as Caple (2018, 102–103) notes, “examining only one semiotic mode elides the com- plexity of construction of meaning across modes, and will at best give only a partial picture of what is actually going on in the modal ensemble”. At the same time, the study shows the considerable gains to be had in triangulating multiple methods. One strength is that corpus linguistic techniques offer a unique method for down sampling from a larger corpus that may reveal interesting patterns in the data that offer themselves up for closer scrutiny. At the same time, CAMDA shows what can be gained by examining each semiotic mode in turn, as it is deployed in the Instagram post. This study has also brought to light a distinctive and unique social practice in the use of metadata on Instagram. Certain hashtags, e.g. #dogsatpollingstations, are very good 444 H. CAPLE predictors of the visual topic of the Instagram post. Future research could further investigate the properties of the hashtag that make it a good visual marker. The #dogsatpollingstations hashtag is at once both generic and very specific. It combines a generic noun “dogs”, which invites community with all dogs, with a very specific circumstance of location that immediately closes down the context in which this community may be formed. It would also be interesting to see how abstract a hashtag can become before it loses the ability to predict the visual content of the post. To conclude, this paper demonstrates the multisemiotic affiliative and distancing strategies that were put to use as social media users convened with each other around the 2016 Australian federal election. Instagrammers do this through the clever combination of words and images. The fact that both Labor and the Greens received mostly favorable references while the Liberal Party was roundly criticized does reflect the conversations that were simultaneously happening in the news media. It also reflects the results of the election. Issues such as marriage equality, health and education were key talking points among the people posting to #dogsatpollingstations on Instagram on the day of the election, as well as in the news media during the election campaign. These are policy issues for the more progressive, left-of-center parties in Australia, parties which made significant gains in the 2016 election (the Australia Labor Party gained 14 seats). The Liberals’ campaign centerd on more traditional conservative policies: jobs and growth, and tax breaks for big business. Issues such as marriage equality were to be dealt with through an extremely unpopular plebiscite, and the potential privatization of health care under the Liberals was used by the Labor Party as a scare campaign. While the Liberal Party did go on to win the election, they did so on a gamble that did not pay off for them, and the dogs of Instagram and their human overlords made it very clear in their posts where their allegiances lay.

Notes 1. The study is part of a larger project investigating the ways in which citizens and organizations outside of journalism are re-shaping and re-defining photojournalistic practice through their engagement with the digital economy (ARC DECRA Project ID: DE160100120) and addresses the project aim of examining the extent to which the Australian news media is in line with the expression of public sentiment through social media platforms on events of cultural, political, and historical significance. 2. Emoticons, like those used in this example are a very common feature of social media. However, given the space constraints of this paper it is not possible to account for their use here (but see Vidal, Gastón, and Jaeger 2016 on their use and meaning on Twitter). 3. The democracy sausage is a uniquely Australian election phenomenon, and is viewed by many as the only incentive to vote. In its classic form, it consists of a sausage (with fried onions and various sauces optional) encased in a slice of white bread (see Figure 1), and is sold at polling stations on the day of the election in order to raise funds for primary schools. 4. Four word lists were compiled using AntConc. 1: Using the software’s default settings (i.e. the # symbol was ignored, so e.g. all instances of #election (20) and election (15) were counted together under the word form election (35)). 2: With User-Defined Token Definitions to include #. This separated out the hashtagged form from the non-hashtagged form of a word. 3: Only non-hashtagged word forms (using a stop list of all hashtagged word forms in the dataset). This gave only words that appeared in the verbal text component of the caption. 4: Only hashtagged word forms. SOCIAL SEMIOTICS 445

Acknowledgements I would like to acknowledge the support and assistance that Monika Bednarek and Laurence Anthony provided with the corpus linguistic analysis presented in this article.

Disclosure statement No potential conflict of interest was reported by the author.

Funding This work was supported by the Australian Research Council under the Discovery Early Career Research Award [DE160100120].

Notes on contributor Helen Caple is an Australian Research Council DECRA Fellow andSeniorLecturerinJournalismattheUni- versity of New South Wales, Sydney, Australia. Her research interests center on news photography, text- image relations and discursive news values analysis. She is currently exploring the role of citizen photography in contemporary journalism. Helen has published in the area of photojournalism and social semiotics, including a monograph with Palgrave Macmillan, Photojournalism: A Social Semiotic Approach (2013). She is also the co-author (with Monika Bednarek) of two books examining the news media: News Discourse (2012, Continuum), and The Discourse of News Values (2017, Oxford University Press).

ORCID Helen Caple http://orcid.org/0000-0003-3215-076X

References About Us. 2017. Instagram, Accessed December 10, 2017. https://www.instagram.com/about/us/. Anthony, L. 2014. AntConc (Version 3.4.4) [Computer Software]. Tokyo, Japan: Waseda University. http://www.antlab.sci.waseda.ac.jp/. Baker, P. 2006. Using Corpora in Discourse Analysis. New York/London: Continuum. Bednarek, M., and H. Caple. 2014. “Why do News Values Matter? Towards a New Methodological Framework for Analysing News Discourse in Critical Discourse Analysis and Beyond.” Discourse & Society 25 (2): 135–158. doi:10.1177/0957926513516041. Bednarek, M., and H. Caple. 2017. “Introducing a New Topology for (Multimodal) Discourse Analysis.” Proceedings from the 2017 International Systemic Functional Linguistics Congress, Wollongong, Australia. Broersma, M., and T. Graham. 2012. “Social Media as Beat: Tweets as a News Source During the 2010 British and Dutch Elections.” Journalism Practice 6 (3): 403–419. doi:10.1080/17512786.2012. 663626. Bruns, A., and J. Burgess. 2011. “#ausvotes: How Twitter Covered the 2010 Australian Federal Election.” Communication, Politics & Culture 44 (2): 37–56. Bruns, A., and J. Burgess. 2015. “Twitter Hashtags from ad hoc to Calculated Publics.” In Hashtag Publics: The Power and Politics of Discursive Networks, edited by N. Rambukkana, 13–28. New York: Peter Lang. Bruns, A., and T. Highfield. 2013. “Political Networks on Twitter: Tweeting the Queensland State Election.” Information, Communication & Society 16 (5): 667–691. doi:10.1080/1369118X.2013.782328. 446 H. CAPLE

Burgess, J., and A. Bruns. 2012. “(Not) the Twitter Election: The Dynamics of the #ausvotes Conversation in Relation to the Australian Media Ecology.” Journalism Practice 6 (3): 384–402. doi:10.1080/17512786.2012.663610. Caple, H. 2018. “Analysing the Multimodal Text.” In Corpus Approaches to Discourse: A Critical Review, edited by C. Taylor, and A. Marchi, 85–109. London: Routledge. Caple, H. in progress. “Image-centric Practices on Instagram: Subtle Shifts in Footing.” In Shifts Towards Image-Centricity in Contemporary Multimodal Practices, edited by H. Stöckl, H. Caple, and J. Pflaeging. London: Routledge. Coleman, S., and J. G. Blumler. 2009. The Internet and Democratic Citizenship: Theory, Practice and Policy. Cambridge: Cambridge University Press. Davidson, H. 2016. “Election Result ‘Muddle’ is Turnbull’s Fault, Says NT Labor MP Warren Snowdon.” The Guardian Australia, July 3. Accessed April 6, 2017. https://www.theguardian.com/australia- news/2016/jul/04/election-result-muddle-turnbulls-fault-nt-labor-mp-warren-snowdon. Davis, R. 2009. Typing Politics: The Role of Blogs in American Politics. New York: Oxford University Press. Gibson, R., and M. Cantijoch. 2011. “Comparing Online Elections in Australia and the UK: Did 2010 Finally Produce ‘the’ Internet Election?” Communication, Politics & Culture 44 (2): 4–17. Gibson, R., W. Lusoli, and S. Ward. 2008. “The Australian Public and Politics Online: Reinforcing or Reinventing Representation?” Australian Journal of Political Science 43 (1): 111–131. doi:10.1080/ 10361140701842607. Grant, W. J., B. Moon, and J. Busby Grant. 2010. “Digital Dialogue? Australian Politicians’ Use of the Social Network Tool Twitter.” Australian Journal of Political Science 45 (4): 579–604. doi:10.1080/ 10361146.2010.517176. Highfield, T. 2015. “Memeology Festival 04. On Hashtaggery and Portmanteaugraphy: Memetic Wordplay as Social Media Practice.” Culture Digitally November Issue. Highfield, T., and T. Leaver. 2015. “A Methodology for Mapping Instagram Hashtags.” First Monday 20 (1), http://firstmonday.org/ojs/index.php/fm/article/view/5563/4195. Hochman, N., and L. Manovich. 2013. “Zooming into an Instagram City: Reading the Local Through Social Media.” First Monday 18 (7): 1–51. http://firstmonday.org/ojs/index.php/fm/article/view/ 4711/3698#p1. Jane, E. A. 2014a. “‘Your a Ugly, Whorish, Slut’–Understanding E-Bile.” Feminist Media Studies 14 (4): 531–546. doi:10.1080/14680777.2012.741073. Jane, E. A. 2014b. “‘Back to the Kitchen, Cunt’: Speaking the Unspeakable About Online Misogyny.” Continuum: Journal of Media & Cultural Studies 28 (4): 558–570. doi:10.1080/10304312.2014. 924479. Jane, E. A. 2017. Misogyny Online: A Short (and Brutish) History. London: Sage. Jewitt, R. 2009. “The Trouble with Twittering: Integrating Social Media into Main-Stream News.” International Journal of Media and Cultural Politics 5 (3): 233–246. Karp, P. 2016. “Election Shows Sajority Support Same-Sex Marriage, LGBTI Groups Claim.” The Guardian Australia, July 3. Accessed April 6, 2017. https://www.theguardian.com/australia-news/ 2016/jul/03/election-shows-majority-support-same-sex-marriage-lgbti-groups-claim. Kenny, M., and J. Massola. 2016. “Australian Federal Election 2016: Voters Walk Away from Malcolm Turnbull, Results on Knife’s Edge.” Sydney Morning Herald, July 3. Accessed April 6, 2017. http:// www.smh.com.au/federal-politics/federal-election-2016/australian-federal-election-2016-erratic- swings-as-malcolm-turnbull-bill-shorten-sweat-on-result-20160702-gpwz6y.html. Kreiss, D. 2015. “The Networked Democratic Spectator.” Social Media + Society 1 (1): 1–2. doi:10.1177/ 2056305115578876. Kreiss, D. 2016. “Seizing the Moment: The Presidential Campaigns’ Use of Twitter During the 2012 Electoral Cycle.” New Media & Society 18 (8): 1473–1490. doi:10.1177/1461444814562445. Kress, G. 2010. Multimodality: A Social Semiotic Approach to Contemporary Communication. London/ New York: Routledge. Kress, G., and T. J. van Leeuwen. 2006. Reading Images the Grammar of Visual Design. 2nd ed. London: Routledge. Lankshear, C., and M. Knobel. 2006. New Literacies: Everyday Practices and Classroom Learning. 2nd ed. New York: Open University Press. SOCIAL SEMIOTICS 447

Larsson, A. O., and H. Moe. 2012. “Studying Political Microblogging: Twitter Users in the 2010 Swedish Election Campaign.” New Media & Society 14 (5): 729–747. doi:10.1177/1461444811422894. Leslie, T. 2016. “What. Just. Happened?” Australian Broadcasting Corporation, News, July 5. Accessed April 6, 2017. http://www.abc.net.au/news/2016-07-03/election-results-what-just-happened/ 7553916. Macnamara, J. 2011. “Pre and Post-Election 2010 Online: What Happened to the Conversation?” Communication, Politics & Culture 44 (2): 18–36. McGregor, S. C., and R. R. Mourão. 2016. “Talking Politics on Twitter: Gender, Elections, and Social Networks.” Social Media + Society 2 (3): 1–14. doi:10.1177/2056305116664218. Mitchell, G., and T. McIlroy. 2016. “Federal Election 2016: ‘Fake Tradie’ Andrew MacRae Reveals How He Got the Gig.” Sydney Morning Herald, June 21. Accessed March 3, 2017. http://www.smh.com. au/federal-politics/federal-election-2016/federal-election-2016-fake-tradie-andrew-macrae-says- hes-the-real-deal-20160621-gpo7tz.html. Murphy, K., and P. Karp. 2016. “Malcolm Turnbull Cannot Command His Party and Should Resign, Bill Shorten Says.” The Guardian Australia, July 4. Accessed April 6, 2017. https://www.theguardian. com/australia-news/2016/jul/04/malcolm-turnbull-cannot-command-his-party-and-should- resign-bill-shorten-says. Papacharissi, Z., ed. 2010. A Networked Self: Identity, Community, and Culture on Social Network Sites. New York: Routledge. Small, T. 2011. “What the Hashtag? A Content Analysis of Canadian Politics on Twitter.” Information, Communication & Society 14 (6): 872–895. doi:10.1080/1369118X.2011.554572. van Leeuwen, T. J. 2008. Discourse and Practice - New Tools for Critical Discourse Analysis. New York: Oxford University Press. Velasquez, A., and H. Rojas. 2017. “Political Expression on Social Media: The Role of Communication Competence and Expected Outcomes.” Social Media + Society 3 (1): 1–13. doi:10.1177/ 205630511769652. Vidal, L., A. Gastón, and S. R. Jaeger. 2016. “Use of Emoticon and Emoji in Tweets for Food-Related Emotional Expression.” Food Quality and Preference 49: 119–128. doi:10.1016/j.foodqual.2015.12.002. Zappavigna, M. 2011. “Ambient Affiliation: A Linguistic Perspective on Twitter.” New Media & Society 13 (5): 788–806. doi:10.1177/1461444810385097. Zappavigna, M. 2012. Discourse of Twitter and Social Media. London: Continuum. Zappavigna, M. 2014. “enjoy your snags australia … oh and the voting thing too #ausvotes #auspol”: Iconisation and Affiliation in Microblogging During Election Campaigns.” Global Media Journal: Australian Edition 8 (2): 1–16. Zappavigna, M. 2015. “Searchable Talk: The Linguistic Functions of Hashtags.” Social Semiotics 25 (3): 274–291. doi:10.1080/10350330.2014.996948. Zappavigna, M. 2016. “Social Media Photography: Construing Subjectivity in Instagram Images.” Visual Communication 15 (3): 271–292. doi:10.1177/1470357216643220.