The Arbutus Review • Fall 2015 • Vol. 6, No. 1 #Activism: Identity, Affiliation, and Political Discourse-Making on Twitter Alexah Konnelly ∗ The University of Victoria alexahk@uvic.ca Abstract Twitter is increasingly recognized for having transformative potential for group advocacy. It also acts as a forum for spreading awareness and information on social justice (or activist) movements, as well as for dialogue between users on a given social justice subject. This study examines what motivates the use of hashtags in the activist context, and how this usage connects to broader discourses and ideologies. Drawing on a corpus of two prolific activist hashtags – #YesAllWomen and #HeForShe – I employ a range of methodologies and frameworks to tease apart issues of use, affiliation, and context. I operationalize Systemic Functional Linguistics (SFL), an analytical approach concerned with linguistic choices and how language is structured to achieve socio-cultural meanings, to analyze the engagement, meanings, and functions manifest in the dataset. The quantitative results are interpreted within the framework of Feminist Critical Discourse Analysis. I argue that hashtags are both linguistic and social facilitative devices, employed by users to assert their collective identity and political affiliation. Editors note: In academia, occasionally researchers delve into controversial topics which can include material considered to be offensive to some. When researching discussions on such contentious issues, disturbing and offensive matter can arise in the data set. Some of this data appears in this article in order to provide evidence for the author’s analysis. The opinions expressed in these pieces of data do not necessarily represent those of the University, the publisher, the editorial board, the editors, the journal staff, and the peer reviewers. Keywords: Hashtags; Twitter; electronically-medicated communication; Sociolinguistics; activism I. Introduction A s a microblogging platform, Twitter includes communication practices not necessarily envisioned in its original tag-line question, “What are you doing?” Daily chatter, sharing of information and links, and news reporting (Java et al., 2007) all co-occur with conventions such as the #hashtag and @replies. While Twitter has recently drawn attention for its political potential (e.g. Gruzd & Roy, 2014), research thus far has been restricted primarily to network (e.g., Himelboim et al., 2013), diffusion (e.g., Bastos et al., 2013), and popularity prediction analyses (e.g., Ma et al., 2012). However, Twitter also acts as a forum for spreading awareness and information on social justice (or activist) movements, as well as for dialogue between users on a given social justice subject. This dimension of Twitter remains relatively unexplored. Since users have found new ways to use hashtags to communicate on an interpersonal level (i.e., from one user to another) and ∗This research was conducted under the supervision of Dr. Alexandra D’Arcy, and was supported by the Jamie Cassels Undergraduate Research award (JCURA) through the Vice President Academic and the Learning and Teaching Centre (LTC) at the University of Victoria. I would like to express my sincerest thanks, appreciation, and gratitude to Dr. Alexandra D’Arcy for her guidance and support throughout the course of this project. 1 mailto:alexahk@uvic.ca The Arbutus Review • Fall 2015 • Vol. 6, No. 1 on a community-building level (i.e., building affiliation with others more broadly) (Shapp, 2014, p. 39), hashtags contribute to ongoing discussion of what constitutes ‘community’ in electronically- mediated communication (EMC) (Shapp, 2014, p. 39). This study seeks to explore and identify what motivates the use of hashtags in the activist context, and how such employment connects to broader discourses and ideologies. The activist context (and the specific hashtags, to be discussed below) was chosen, in line with Zappavigna (2011, p. 792), ”to conduct a case study in which field variables, that is, the topic of the tweets, was held relatively constant to afford a rich investigation of meaning-making in a single domain.” Twitter has many communication conventions (e.g., retweeting, favouriting, @replies), but the hashtag is arguably its most powerful. According to Bruns and Burgess, ”the hashtag has proven itself to be extraordinarily high in its capacity for “cultural generativity” (2011, p.3), the diverse ”creative, social, and communicative activities” in which users engage (Burgess, 2012, p. 41). A key example is #BlackLivesMatter. The hashtag first surfaced in 2012 as a response to the acquittal of George Zimmerman, a Florida Neighbourhood Watch coordinator who shot and killed 17 year-old Trayvon Martin (Robertson & Schwartz, 2012). It subsequently drew more attention after the deaths of Micheal Brown in Ferguson and Eric Garner in Staten Island in 2014. That year, the American Dialect Society voted #BlackLivesMatter its Word of the Year (Curzan, 2015), demonstrating that hashtags can develop profound cultural salience. In short, hashtags perform both linguistic and social work, rendering them empirically valid objects of inquiry. Efforts have been made to categorize the hashtag into different types, and a multiplicity of Internet guides are available suggesting the type of hashtag to choose for “greatest effect” (e.g., Van Veen, n.d.). Absent from these guides, however, are political hashtags of any nature. There is a wide variety of hashtags that could be described as political, but which likely perform different functions than one another (e.g., #cdnpoli, for discussing Canadian politics; #POTUS, for discussing the President of the United States; etc.). Therefore, it is unlikely that the term political hashtag itself represents a homogenous, unified category. I propose one category that is relevant for the work presented here: the Cause Hashtag. The Cause Hashtag is used or created with a specific goal in mind—to advance a cause, raise awareness, or rally support for a particular social issue. #BlackLivesMatter is one such example. Given this context, the questions I ask here are: (1) how do users engage with the emergent sociality that is ’hashtagging’? (2) How do hashtags add meaning to a tweet and what function(s) do they serve? And (3), how are the discourses connected to hashtags embedded within broader social ideologies and identities? I argue that hashtags act not only as a meta-message within a tweet, but also as tools of affiliation, political discourse-making, and collective identity-informing. In the course of this research, I encountered many viewpoints. Some of these were offensive, but were kept in my study because they were intrinsic to analyzing the data. These tweets do not reflect my views, but have been included as evidence of the breadth of data collected. Additionally, although the hashtag is used on multiple online platforms and social media (e.g., Facebook, Instagram, Tumblr, Pinterest, Blogger, etc.), I use the term hashtag to refer exclusively its use in the Twitter environment. II. Data and Method To address these questions, I draw on a corpus of Cause Hashtags and employ a range of methodologies and frameworks to tease apart issues of use, affiliation, and context. To answer questions (1) and (2), I operationalize Systemic Functional Linguistics (SFL) (Halliday, 1994) in order to analyze the engagement, meanings, and functions manifested in the tweets. I outline this framework in section IV. To answer question (3), I interpret the quantitative results based on the 2 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 Feminist Critical Discourse Analysis literature. I. Data Two prolific Cause Hashtags are targeted here: #YesAllWomen and #HeForShe. In addition to being viral, these hashtags share two features: their narratives are temporally close to one another, and their respective messages are simultaneously congruent and divergent. II. Temporality and context #YesAllWomen first surfaced in May of 2014 in response to the Isla Vista killings, an attack fuelled by the shooter’s desire for “retribution” against women he alleged had rejected him (Massarella, Rosenbaum, & Greene, 2014, n.p.). The hashtag emerged out of a desire to assert that, while not all men commit violent sexist crimes, all women have experienced some form of violence or harassment from men. #YesAllWomen was intended to focus the discussion on the shooting as an incidence of systemic violence against women, and not simply the isolated acts of one mentally ill individual. #HeForShe originated in September of 2014. It represents the official “solidarity campaign” for gender equality initiated by the United Nations (Monde, 2014, n.p.). The campaign, introduced by UN Women Goodwill Ambassador Emma Watson, seeks to facilitate greater involvement of men and boys in the achievement of gender equality worldwide. In the words of Ambassador Watson, #HeForShe is men’s “formal invitation” to participate in ending gender-based discrimination (Watson, 2014, n.p.). III. Congruence and Divergence #YesAllWomen and #HeForShe are congruent in that they both address systemic gender-based violence and/or discrimination. They are divergent in that they target different audiences. Whereas #YesAllWomen focuses primarily on women sharing their stories and their relationships with institutionalized sexism, #HeForShe focuses primarily on men and their relationship with the same thing. However, #YesAllWomen has been criticized by men who feel that it “disparages men in [a] grotesquely unfair fashion” (Hemingway, 2014, n.p.), and #HeForShe has been criticized by women who are concerned about the implications of a gender equality movement that “relies on the primary oppressor” to achieve its goals (Deaver, 2014, n.p.). Thus, the two tags are valuable subjects of comparison because they target the same topic from contrasting perspectives and sources. Tweets containing #YesAllWomen and #HeForShe were extracted from Twitter using Tweet Archivist (https://www.tweetarchivist.com/). The first tweets downloaded are those from the 24-hour timeframe in which the search was executed; afterwards, the spreadsheet is updated hourly. Given the dynamic nature of Twitter, even a 24-hour timeframe can result in thousands of tweets meeting a particular query. For this analysis, only the initial data were used—any tweets from the hourly updates were excluded. Tweets in languages other than English were also excluded. This was done to restrict the analysis to English-speaking communities of practice. Finally, straightforward, unaltered retweets (“RTs”) were removed (although RTs in which the user added text to the tweet were retained), to ensure, as best as possible, that all tweets contain the intellectual property of the user who tweeted them. This approach resulted in 370 tokens of #YesAllWomen (reflecting 24 consecutive hours of Twitter discourse from October 2014) and 240 tokens of #HeForShe (reflecting 24 consecutive hours of Twitter discourse from January 2015). This method was employed in order to obtain a random sample reflective of the entire set. 3 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 IV. Theoretical Framework The theoretical framework adopted for this study is Systemic Functional Linguistics (SFL), which explores “how social worlds are [. . . ] established in and through language” (Kazemian & Hashemi, 2014, p. 1179). In other words, SFL focuses on how language is structured to achieve socio-cultural meanings, often through the analysis of texts in consideration to the social context in which they occur (Kazemian & Hashemi, 2014, p.1179). According to SFL, language enacts three metafunctions: an interpersonal function of negotiating relationships, an ideational function of enacting experience, and a textual function of organizing information (Halliday & Matthiessen, 2004). These three functions relate rather neatly to the central questions outlined so far. Question (1), “how does the hashtag add meaning to the tweet?,” is textual, organizing information. Question (2), “what function(s) does it serve for users, not just in interaction with others but in ‘solo’ messages to an imagined audience?,” relates to the interpersonal and the negotiating of relationships. And Question (3), “in what ways are the discourses that surround the hashtag embedded within broader social ideologies and identities?,” is ideational, enacting experience. To analyze the meanings manifest in the tweets, I will also draw on the Theory of Appraisal, developed by Martin and White (2005) within the SFL paradigm. This theory deals with ‘evaluative language’. According to Zappavigna (2011, p. 794), “evaluation is a domain of interpersonal meaning where language is used to build power and solidarity by adopting stances and referring to other texts.” Since Cause Hashtags are used or created to advance a cause, raise awareness, or rally support for a particular social issue, ‘building power and solidarity by adopting stances’ is arguably their foundational function. Use of Cause Hashtags regularly involves a process of ‘referring to other texts,’ often through the sharing of links related to the hashtag or its message. Thus, evaluative language can be said to be a domain intrinsic to #YesAllWomen and #HeForShe. The Theory of Appraisal considers how text construes emotional language in three areas: attitude (making evaluations), engagement (bringing other voices into the text) and graduation (scaling up or down evaluations). As graduation did not operate significantly with either hashtag, it has been omitted from this work and will not be discussed further. Each of these areas can be understood as ‘systems networks’ (Zappavigna, 2011) containing additional choices. Accordingly, attitude was divided into three factor groups: attitude-type, explicitness, and attitude- polarity. Attitude-type was coded as either affect (expressing feeling or emotion), as in (1), judgment (expressing opinion, ethics, or morality), as in (2), or appreciation (expressing aesthetics or value/worth), as in (3). The categories affect, judgment, and appreciation could conceivably co-occur. However, in this dataset, they are mutually exclusive. Whether or not this is due to the character limit on tweets or the ideological nature of the hashtag’s message is unclear, and I leave this open to future research. (1) best way to get me in a bad mood is #YesAllWomen hashtag (2) #GenderEquality is not only a women’s issue. . . So we need men to support feminism.[link] #HeForShe (3) She’s just such a great human being all around #HeForShe [link] Explicitness was coded for inscribed (explicit meaning, easily discernible), as in (4), or invoked (latent meaning, requires deduction), as in (5). Attitude-polarity was coded as either positive-attitude (user agrees with the message of the hashtag), as in (6), or negative-attitude (user does not agree), as in (7). (4) I’m a boy, and I’m 12, but it’s obvious enough that this is right. @EmWatson #HeForShe @HeforShe [link] 4 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 (5) When I do a ’feminist post’ on social media, I hope that men will respond positively. Men will benefit from gender equality too! #HeForShe (6) Yes I’m a guy, and I do support feminism! #YesAllWomen (7) #YesAllWomen need a good beating to put them in their place What poses a challenge for coding attitude polarity is that tweets can appear attitudinally ambigu- ous when users do not explicitly state their ideological affiliation. In fact, sometimes responses are not related to the intended topic of the hashtags at all, as in (8)–(10). (8) @TwitterUser1 If I consent to have sex with a female & later find out he-shes a post op transexual=(man) Did he-she rape me? #yesallwomen (9) #If she says no and you still do it, you’re either a baller or a rapist #YesAllWomen (10) #YesAllWomen think that #hitlerdidnothingwrong Frequently these tweets take the form of a rape joke or rhetorical question, and sometimes both, as in (8). Given that these hashtags are intended to raise awareness for systemic violence and discrimination against women, tweets that trivialize or derail this discussion, as in (8)–(10), were interpreted as indicating a negative attitude polarity. Finally, engagement was coded as mono-glossic (non-conversational; ‘solo’), as in (11), or hetero- glossic (conversational; contains @username, a RT, or is otherwise directed at an individual), as in (12). (11) If not me, who? If not now, when? #HeForShe (12) @TwitterUser @TwitterUser #YesAllWomen Exactly, "boys will be boys" doesn’t cut it any- more. Where the information was available, tokens were also coded for user gender. Since previous research in EMC has shown a relationship between gender, hashtag use, and Twitter behavior (e.g., Bamman, Eisenstein, & Schnoebelen, 2012; Shapp, 2014), whether it is also a factor in the use of #YesAllWomen and #HeForShe is of interest, particularly in light of their innately gendered narratives. Gender is challenging to code for, however, since most users do not include any gender-identifying information on their Twitter profiles (cf. Table 1). Those who did use explicit self-identifying language in either their tweets or their Twitter profiles were coded accordingly; the rest were coded unknown. To investigate how patterns of hashtag use intersected with gender identity, cross-tabulations of gender with each internal predictor were performed.2 The quantitative analysis explores the ways in which users engage with the hashtags on Twitter. The model used here is proportional, examining the distribution of factors (i.e., attitude, explicitness, etc.) within and across the two Cause Hashtags. The qualitative portion of this study is informed by Feminist Critical Discourse Discourse Analysis (FCDA), which focuses on gendered social practices. Since the hashtags in question are definitionally ‘about’ gender, the task is to examine how power and ideology are discursively produced, resisted, and counter-resisted through textual representations that have had their meaning components ‘broken down’ by Appraisal Analysis. 1The names of Twitter users have been removed to protect the identity of the Authors of Tweets used in this study. 2The only genders that were self-identified within this dataset are men and women. Since the ‘unknown’ gender group is too internally heterogeneous to make any meaningful extrapolations, it is therefore excluded from examination of gender. This brings the number of #HeForShe tokens to 81 and #YesAllWomen tokens to 187 for this part of the discussion. 5 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 Table 1: User Sample by Gender Gender #HeForShe YesAllWomen users,n tweets,n users,n tweets,n Women 35 41 46 122 Men 33 40 29 65 Unknown 110 159 111 183 Total N 178 240 186 370 V. Distributional Results In this section, I present the distributions of the hashtags according to internal (i.e., attitude, explic- itness) and external (i.e., gender) factors, in order to expose the discursive strategies and meanings constructed through their use. Distribution of tweets based on attitude type, attitude polarity, engagement, and gender in particular reveal certain facts that contribute to an understanding of how each hashtag functions, as well as how participation in each of the hashtags differs. Figure 1: Distribution of #HeForShe and #YesAllWomen by Attitude Type Given that #HeForShe and #YesAllWomen share the goal to raise awareness for a social justice- related issue, it follows that the majority of tweets express an opinion, judgment, or relate to ethics. Figure 1 reports the results for Attitude Type. As expected, the majority of tokens for both hashtags encode judgment. #YesAllWomen has a higher proportion of tweets indicating a judgment attitude type, yet for both hashtags these attitudes generally express opinions related to the message, and positive judgments for both are quite similar: they tend to express agreement with the message. The two diverge, however, when it comes to negative judgments. Disagreement with #YesAllWomen is expressed indirectly—for example, claims of sexism are challenged (13, 14), or are hyperbolic (15). In contrast, negative judgments of #HeForShe challenge the usefulness of the hashtag (16), contest its goals (17), or accuse it of being one-sided (18): (13) So..are you saying that you’ve NEVER checked out a guy before? Never? #YesAllWomen (14) #yesallwomen because some women fake rape (15) I have a penis therefore whatever I have to say is wrong. #YesAllWomen (16) #heforshe support your feminism, just as long as you look like Emma Watson #mysogyny #radfem #feminism #yesallwomen 6 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 (17) #heforshe support your feminism as long as your idea of freedom is making cupcakes & wanting to be a princess. #misogyny #radfem #YesAllWomen (18) @TwitterUser #heforshe is worthless without #sheforhe. #YesAllWomen and #HeForShe have different sources: #YesAllWomen represents a grassroots effort with no official leader or spokesperson, while #HeForShe is largely attributed to United Na- tions Ambassador Emma Watson. This difference is reflected in the use of affect and appreciation attitude types. Many #HeForShe tweets are addressed directly to Emma Watson and express an affect (19-20) or appreciation (21-22) attitude type: (19) @EmWatson’s UN speech about gender inequality inspires me. So brave, empowering and moving. #HeForShe (20) @EmWatson I hope that we support #heforshe Campaign here is Saudi Arabia :) (21) @EmWatson@ELLEBelgique You look soo beautiful, what an incredible cause! #HeForShe #EmmaWatson [link] (22) Emma Watson is cute’n sexy. She makes me wish I was a big brawny badass so I could protect her from rapists and terrorists. #HeForShe With #YesAllWomen, however, there is a smaller proportion of affect-oriented tweets. These tend to either express emotions regarding personal experience, as in (23), or gender-based discrimination in general, as in (24): (23) Did not enjoy seeing my friend being harassed by two drunken sexists though.#YesAllWomen #streetharassment (24) @TwitterUser @TwitterUser #YesAllWomen I’m fed up with feeling unsafe in the world that I live in, no more! When cross-tabulated with gender, as in Figure 2a (left), women use #HeForShe to express a wider range of attitude types than men do. Regardless, #HeForShe is significantly more likely to encode a judgment attitude type than any other (χ(1) = 7.42, p < 0.05). This is consistent with the assumption that Cause Hashtags, which characteristically represent a goal to raise awareness for a social justice-related issue, would be primarily opinion-, judgment-, or ethics-oriented. Figure 2: Distribution of Attitude by Gender in #HeForShe tokens (left) and #YesAllWomen (right) 7 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 Reported in Figure 2b (right), there is less variability of attitude type for #YesAllWomen. For both genders, judgment again comprises the vast majority of tokens (women: 84 %, n=102; men: 88%, n=57), but the likelihood of judgment use is not statistically significant (χ(1) = 0.56, p = 0.45). Affect and appreciation are marginalized in the data. Broadly construed, #HeForShe involves more affect- and appreciation-oriented engagement than #YesAllWomen does, which can be connected to the origins of the hashtags. #HeForShe has a spokesperson, who draws affect- and appreciation-based attention from users. #YesAllWomen, as a grassroots movement, has no such leader. As such, the smaller proportions of affect-oriented tweets with the hashtag tend to either express emotions regarding personal experiences or gender-based discrimination. Figure 3: Distribution of #HeForShe and #YesAllWomen tokens by Attitude Polarity Figure 3 reports the results for Attitude Polarity. In terms of both overall distribution and distribution by gender (cf. Figures 4a and 4b), the hashtags show different levels of attitude polarity. For example, 89% (n=213) of all #HeForShe tokens indicate a positive attitude polarity, a figure that drops to 64% (n=235) for #YesAllWomen. When cross-tabulated with gender, distribution of #HeForShe by attitude polarity is similar for both men and women—the orientation is primarily positive (women at 95%, n=39; men at 90%, n=36). However, the situation is markedly different for #YesAllWomen. Women have a primarily positive attitude polarity (93%, n=113), but men have a primarily negative attitude polarity (78%, n=51). This association is highly significant (χ(1) = 98.34, p < 0.01). Figure 4: Distribution of attitude polarity by gender in #HeForShe tokens(left) and #YesAllWomen (right) The role of gender in the patterning of attitude polarity can be connected to the target audience 8 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 for each hashtag. While #HeForShe explicitly includes men as part of the message, #YesAllWomen excludes them (though they remain an important part of the implicit rhetoric). This is likely why men respond negatively in the majority to #YesAllWomen, but in the minority to #HeForShe— #YesAllWomen has a solidarity-building function whereby users (specifically women) build a collective identity characterized by their shared personal experiences. However, rather than using a different hashtag to encode their dissent (e.g., #NotAllMen), men with a negative attitude polarity towards the hashtag(s) continue to use them. That is, they specifically employ #YesAllWomen to express their dissent. This shows that a hashtag’s ‘core demographic’ (men for #HeForShe; women for #YesAllWomen) is therefore not the only participant group. Of course, disagreement with a hashtag goes beyond target demographics. There are certain discourses that both preclude and inform the use of these hashtags—that systemic gender-based violence or discrimination does not exist (25), that it is equally experienced by men and women (26), or that it does exist, but women are deserving of it (27). While language is central in producing culture, discourse can express or reveal meanings that have developed over time, “prior to a particular linguistic act” (Avlesson and Karreman, p. 1137). (25) You want a pay raise? Work hard and then ask for it. You want to stop catcalling? Don’t give them the attention. #YesAllWomen (26) @TwitterUser I know #YesAllWomen don’t care about that sexist law, which is why I don’t care about sexism towards women. Tit-for-Tat (27) @TwitterUser @yesallwomen Actions speak louder than words! Are you a celibate virgin? If not you’ve been saying YESSS in denial #yesallwomen Consequently, it follows that some users respond strongly to the hashtags. Both #YesAllWomen and #HeForShe have a clear connection to broader discourses and ideologies related to their message. When users agree or disagree with the hashtags, they are also agreeing or disagreeing with the discourses and/or ideologies connected to them, a process described by Zappavigna (2013, p. 789) as affiliation. Figure 5: Distribution of attitude polarity by gender in #HeForShe tokens Next, both hashtags pattern similarly with respect to explicitness (Figure 5)—both favour an inscribed meaning. Neither is more likely to encode one than the other (χ(1) = 0.26, p = 0.61). When cross-tabulated with gender, the overall patterns displayed in Figure 5 are replicated: differ- ences between the genders are not significant (#HeForShe χ(1) = 0.53, p = 0.47; #YesAllWomen χ(1) = 1.8441, p = 0.17). Notably, both genders use latency primarily in the form of rhetorical 9 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 questions, yet the nature of such questions differs. While all rhetorical questions in the dataset produced by women express frustration over a personal experience (28–29), those produced by men overlap with a negative attitude polarity (see (8) and (30)). (28) So I can’t wear lipstick to work without my misogynistic coworker asking me which one of my male coworkers I’m dressed up for? #YesAllWomen (29) @TwitterUser #YesAllWomen So women are being heard, huh? (30) When did female #empowerment become female infantilization? [link] #feminism #yesall- women #heforshe #victorian Thus, latency is a resource applied in different ways by both genders in their engagement with the hashtags. In terms of overall distribution, #YesAllWomen and #HeForShe pattern identically with respect to engagement. When cross-tabulated with gender, this overall pattern remains effectively stable for #YesAllWomen—no gender effect is evident (Figure 6b; χ(1) = 1.7045, p = 0.19). However, important gender-based variation emerges for #HeForShe, where tokens produced by men have a higher percentage of hetero-glossic (i.e., ‘conversational’) engagement than those by women: 58% (n=23) versus 32% (n=13) respectively. This association is significant (χ(1) = 5.4552, p < 0.05). Figure 6: : Distribution of Engagement by gender in #HeForShe tokens (left) and #YesAllWomen tokens (right) Differences in levels of mono- and hetero-glossia across genders may reflect their functions. Women tend to use the hashtags to facilitate a mono-glossic narrative to discuss their own personal experiences (31–32), whereas men tend to use them to engage with women about those experiences, either positively (33) or negatively (34); for #HeForShe, the audience is often Ambassador Emma Watson or one of the United Nations or HeForShe campaign accounts (35). (31) Thanksgiving’s next week. As a pregnant full-time working mum, I’m thankful for a husband that views women as equals. #HeForShe (32) Going far, far out my way to go home because one dude refused to take ”Please leave me alone” for an answer. #yesallwomen (33) Good thoughts & strength out to @TwitterUser Her & her family received specific, graphic, death threats for speaking out. #YesAllWomen (34) @TwitterUser the blaming of your imperfections on all men is the definition of feminism. #YesAllWomen 10 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 (35) As a #HeForShe, I’m committed to #genderequality. I invite you to stand with me. @HeFor- She [link]. Props to @EmWatson To summarize, these two hashtags pattern quite differently, and sometimes in significant ways. The hashtags appear to share the following characteristics: both encode a primarily judgment- oriented attitude type, as well as a primarily inscribed explicitness and mono-glossic engagement. At the same time, there are statistically significant associations between gender and attitude polarity and gender and engagement that operate distinctly across #HeForShe and #YesAllWomen. With respect to gender and attitude polarity, agreement with #YesAllWomen is the inverse for men and women, an association that is highly significant. Finally, analyzing the patterning of engagement along with gender reveals that the hashtags perform different functions for both genders. III. Discussion I now return to the original research questions. How do users engage with the emergent sociality that is ‘hashtagging’? How do hashtags add meaning to the tweet in this domain, and what function(s) do they serve for users? And finally, what are the ways in which the discourses connected to hashtags are embedded within broader social ideologies and identities? In section 3.1, I contextualize the engagement with hashtagging in the Cause Hashtag sub-domain in terms of collective identity. In section 3.2, I outline the function and meaning of the hashtags as evidenced by the analysis. I. Engagement: collective identity Despite the similar message of #YesAllWomen and #HeForShe (i.e., addressing systemic gender- based violence and/or discrimination), these hashtags show different levels of engagement across genders. A tag’s ‘core demographic’—men in the case of #HeForShe, women in the case of #YesAllWomen—are never the only participants. Those that fall outside of the core also participate, and they make their feelings about being ‘excluded’ known: this is evidenced by the significant associations between gender and attitude polarity (cf. Fig. 4a and Fig 4b). Crucially, these users maintain the hashtag; they do not, for example, deploy a different hashtag to encode their dissent, e.g., #NotAllMen. This shows that a given hashtag is a linguistic and social resource employed by users irrespective of whether or not they identify with it—in fact, some may very well use it specifically because they do not identify with it. Users who do not agree with the hashtag can employ it to directly engage with members of the opposing side (i.e., those who do agree with the hashtag). Preservation of the hashtag thus becomes a means by which users can speak to the community. Clearly, hashtag use is more complex than being used “without rules, sense, or intelligence” as previously argued by Biddle (2011, n.p.). This is discussed in greater detail below. In characterizing hashtagging as an emergent sociality, there is also the question of what kind of sociality is at stake. In the context of this particular sub-domain, it appears that hashtags are acting as markers of collective identity. Melucci (1996, p. 44) defines collective identity as “an interactive and shared definition produced by several individuals (or groups at a more complex level) and concerned with the orientations of action and field of opportunities and constraints in which the action takes place.” Taylor, and Whittier (1992, p.105) argue that, in addition to collective identity involving a group’s shared definition about its situation and place in the larger society, it also has three additional characteristics: boundaries, consciousness, and negotiation. Boundaries separate the group from the opposition by emphasizing differences between group members and members of the opposition; in so doing, they emphasize the minority group’s shared perceptions 11 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 and sentiments of being distinct from the dominant group. This is evidenced in #YesAllWomen tweets such as those in (36)–(38). (36) Depending on where we live & what work we do; women get threatened with rape & murder. #YesAllWomen #WhyINeedFeminism (37) @TwitterUser #YesAllWomen It shouldnt, but the lens through which women are viewed, is quite different than how men are viewed.#yesallwomen (38) #yesallwomen because nobody ever asks how many people guys fuck to get where they are.-.-’ In each of these tweets, the users emphasize the differences between themselves and the opposition (in this case, men): in (41), the difference is that women receive threats of rape and murder that men do not; in (42), the difference is ‘the lens’ through which women are viewed compared to men; and in (43), the difference is in the questions that women are asked that men are not. However, the boundaries established by #HeForShe differ from those established by #YesAllWomen. While the opposition in #YesAllWomen tweets is men, in #HeForShe tweets, it is broader: the boundary is between those who support gender equality versus those who do not. A level of consciousness of the members of a minority group is also required for a collective identity to become established. This means that a group becomes aware of itself through a series of self-re-evaluations of shared experiences, shared opportunities, and shared interests (Ayers 2003, p. 151). This is achieved for both hashtags primarily through discussion of either the user’s personal experience (39–40) or their shared belief in gender equality (41). (39) Some guy catcalled me, snapped his fingers at me and called me Madonna while making kiss faces. I AM NOT AN OBJECT! #YesAllWomen @TwitterUser (40) As a man, I find it noteworthy how infrequently my employer’s religious convictions affect my health coverage.#HeForShe#YesAllWomen (41) As a #HeForShe, I’m committed to #genderequality. I invite you to stand with me.@HeForShe [link] Finally, collective groups use a process of negotiation to build their collective identity, often through dialogue amongst themselves and/or with the opposition. This is evidenced in the many hetero-glossic tweets in the data, where users engage with one another (42–43) or an out-group member (44) on the topic. (42) @TwitterUser’s support for #HeForShe doesn’t solve any problems at all, but good reaction. we need international help on damned rape cases. (43) @TwitterUser what you think about @EmWatson project #HeForShe? (44) @TwitterUser_ why do I need a man? Because I am a woman??? That’s sexist!! #YesAll- Women In their negotiation with the collective groups (i.e., those who support #YesAllWomen and #HeForShe), users that disagree with the hashtag often directly challenge the boundaries and consciousness of those who support them, evidenced by examples (25) and (26), repeated below. (25) So..are you saying that you’ve NEVER checked out a guy before? Never?#YesAllWomen 12 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 (26) #yesallwomen because some women fake rape In (25), the user challenges the boundaries established by the collective group by implying that women are not that different from men in that they engage equally in what is presumably either street harassment or objectifying behaviour (which they interpret as “checking out”). In (26), the user challenges the consciousness of the collective group by challenging the validity of their shared experiences (in this case, that all cases of rape are real). In all, the engagement in both #YesAllWomen and #HeForShe demonstrates that hashtags can be understood as semiotic linguistic resources: hashtags act as facilitative devices for asserting one’s collective group identity and ideological affiliation. #YesAllWomen is used primarily in this data set to establish a collective identity of women, whereas #HeForShe is used primarily here to establish a more broad collective identity of supporters of gender equality. Ayers (2003) notes that “Shared definitions of right and wrong help a person link her beliefs to the larger group’s same belief, thus attaching the individual to the group” (p. 151), and it appears that the hashtags are a resource that users employ in communicating these beliefs and attaching themselves to their desired group. This also shows that in establishing and maintaining collective identity, hashtags act as a specific resource that users can draw upon and organize their efforts around. According to Fominaya (2010, p. 398), the development of collective identity “is the result of an interaction between more latent and submerged day-to-day activities and visible mobilizations,” crucial arenas in which activists can foster reciprocal ties of solidarity and commitment, clarify their understandings of who they are, what they stand for, and who the opposition is. Hashtags allow users to perform all of these functions not only in real time, but across space and time as well. Communication between users is initiated and organized by use of the hashtag itself, enabling them to form communities of collective group identity and shared ideological affiliation. II. Function and meaning Building on the above discussion, it is now possible to answer question (2), How do hashtags add meaning to the tweet in this domain, and what function(s) do they serve for users? This can be answered in two parts. First, Cause Hashtags clearly have an interpersonal function: users affiliate with values related to the hashtag itself. These values may be affective, ideological, or aesthetic; they may be inscribed or invoked; and they may be negotiated in interaction with other users (i.e., hetero-glossic). Hashtags also function as linguistic devices that are able to mark the topic of evaluation in different ways. They may act as an independent tag marking the entire tweet, they may mark the topic of engagement, and they may be embedded in the syntax of the tweet’s message itself. Ultimately this shows that, in addition to acting as social devices (i.e., having the ability to facilitate the establishment and maintenance of collective identity, outlined in section 3.1), hashtags perform linguistic functions as well. Instead of being stuck in digital statements to “validate them as part of [a] new, mangled syntax” (Biddle, 2011, n.p.), it seems that hashtags fit quite comfortably into the existing syntax of the language. Second, hashtags are not capable of carrying meaning in addition to their initial message, but they do co-occur with a variety of semantic information. Attitude type, attitude polarity, explicitness, and engagement are all encoded in tweets containing both #YesAllWomen and #HeForShe. Yet, the hashtags cannot stand for any of these meanings independently. This means that, aside from the specific message that they encode, the hashtag is restricted by the information added by the user. Therefore, it appears that the contribution of a hashtag (i.e., what semantic content it adds to the tweet when used) involves a combination of its meaning (or message) and function, and the meaning of the text in the tweet itself. 13 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 IV. Conclusion This project has examined the use of Cause Hashtags on Twitter, focusing on #YesAllWomen and #HeForShe. I have argued that hashtags have an unambiguous interpersonal function wherein users affiliate with values related to each hashtag, which are themselves linguistic devices that mark the topic of evaluation in different ways. I have also argued that hashtag use can be understood as a semiotic activity, with hashtags acting as facilitative devices for asserting one’s collective identity and political affiliation. Cause Hashtags, and hashtag activism more generally, may represent a new form of resistance and challenge in the technological world. It is one of many new methods for increasing awareness of advocacy efforts, and allows for “bypassing the gatekeepers by giving a range of advocates, from everyday citizens to multi-million dollar companies, an opportunity to get their messages out to others” (Stache, 2014, p. 2; emphasis in original). Given this potential, analyzing hashtags as linguistic units with the potential of community-building and identity negotiation can contribute to our understanding of “the inevitable linguistic complexity that arises as people commune online” (Zappavigna, 2011, p. 804). As online communication becomes increasingly searchable, the amount of data available to researchers is sure to grow. Hashtags not only develop profound cultural salience, but they also perform social and linguistic work. This research demonstrates that it is possible to analyze hashtags in-depth in both these domains, and that both their meanings and functions can be broken down componentially. The hashtag is a powerful tool, and analytical possibilities are on the rise. There remains plenty of room for future research on this sociolinguistic device. References Ayers, M. D. (2003). Comparing collective identity in online and offline feminist activists. Cyberactivism: Online activism in theory and practice, 145–164. Bamman, D., Einstein, J., & Schnoebelen, T. (2012). Gender in Twitter: Styles, stances, and social networks. Presentation at NWAV 41, Indiana University, Bloomington. Bastos, M., Raimundo, R., & Travitzki, R. (2013). Gatekeeping Twitter: Message diffusion in political hashtags. Media, Culture & Society, 35(2), 260-270. Biddle, S. 2011. How the hashtag is ruining the English language. Retrieved April 19, 2015, from http://gizmodo.com/5869538/how-the-hashtag-is-ruining-the-english-language Bruns, A. & Burgess, J. (2011). The use of Twitter hashtags in the formation of ad hoc publics. Paper presented at the 6th European Consortium for Political Research General Conference, University of Iceland, Reykjavik. Burgess, J. (2012). The iPhone moment, the Apple brand and the creative consumer: “Hackability and usability” to Cultural Generativity. Studying Mobile Media: Cultural Technologies, Mobile Communication, and the iPhone. 28-42. New York: Routledge. Curzan, A. (2015). WOTY2014 Comments. Retrieved January 22, 2015, from http:// chronicle.com/blogs/linguafranca/2015/01/12/woty2014/ 14 Text Box http://dx.doi.org/10.4324/9780203954317 Text Box http://dx.doi.org/10.1177/0163443712467594 Text Box http://dx.doi.org/10.4324/9780203127711 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 Deaver, K. (2014). Why hating the HeForShe campaign doesn’t make me a bad feminist - Role Reboot. Retrieved January 2, 2015, from http://www.rolereboot.org/ culture-and-politics/details/2014-09-hating-heforshe-campaign-doesnt-make-bad- feminist/ Fominaya, C. F. 2010. Collective identity in social movements: Central concepts and debates. Sociology Compass, 4(6), 393-404. Gruzd, A.,& Roy, J. (2014). Investigating political polarization on Twitter: A Canadian perspective. Policy and Internet, 6(1), 28-45. Halliday, M. (1994). An introduction to functional grammar. London: Arnold. Halliday M. & and Matthiessen C. (2004). An introduction to functional grammar. London: Arnold. Hemingway, M. (2014). The ten most asinine things about YesAllWomen. Retrieved January 2, 2015, from http://thefederalist.com/2014/05/28/the-ten-most-asinine- things-about-yesallwomen/ Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-Mediated Communication, 18(1), 40-60. Java, A., Finn, T., Song, X. & Tseng, B. (2007). Why we Twitter: Understanding microblogging usage and communities. Presentation at Joint 9th WBKDD and 1st SNA-KDD Workshop, San Jose, California. Kazemian, B., & Hashemi, S. (2014). Critical discourse analysis of Barack Obama’s 2012 Speeches: Views from Systemic Functional Linguistics and rhetoric. Theory and Practice in Language Studies, 4(6), 1178-1187. Ma, Z., Sun, A., & Cong, G. (2012). On predicting the popularity of newly emerging hashtags in Twitter. Journal of the American Society for Information Science and Technology, 64(7), 1399-1410. Martin J. & White P. (2005). The language of evaluation: Appraisal in English. New York: Palgrave Macmillan. Massarella, L., Rosenbaum, S., & Greene, L. (2014). The vile manifesto of a killer. Retrieved January 4, 2015, from http://nypost.com/2014/05/25/the-twisted-hate-filled-manifesto-of -calif-gunman-elliot-rodger/ Melucci, A. (1996). Challenging codes: Collective action in the information age. Cambridge, MA: Cambridge University Press. Monde, C. (2014). Emma Watson defends feminism in speech at UN meeting. Retrieved January 3, 2015, from http://www.nydailynews.com/entertainment/gossip/ emma-watson- 15 Text Box http://dx.doi.org/10.1111/j.1751-9020.2010.00287.x Text Box http://dx.doi.org/10.1002/1944-2866.poi354 Text Box http://dx.doi.org/10.4324/9780203783771 Text Box http://dx.doi.org/10.1111/jcc4.12001 Text Box http://dx.doi.org/10.4304/tpls.4.6.1178-1187 Text Box http://dx.doi.org/10.1002/asi.22844 Text Box http://dx.doi.org/10.1017/cbo9780511520891 The Arbutus Review • Fall 2015 • Vol. 6, No. 1 defends-feminism-u-n-meeting-article-1.1948222 Robertson, C., & Schwartz, J. (2012). Shooting focuses attention on a program that seeks to avoid guns. Retrieved January 22, 2015, from http://www.nytimes.com/2012/03/23/us/trayvon -martin-death-spotlights-neighborhood-watch-groups.html& Shapp, A. (2014). Variation in the use of Twitter hashtags. Qualifying paper in sociolinguistics, New York University. Sharma, S. (2013). Black Twitter? Racial hashtags, networks and contagion. New Formations, 78(1), 46-64. Taylor, V. & Whittier, N. (1992). Collective identity in social movement communities: Lesbian feminist mobilization. In Frontiers in Social Movement Theory, (pp. 53-65). New Haven, CT: Yale University Press. Van Veen, D. (n.d.). What are the types of hashtags? Retrieved January 3, 2015, from http:// davidvanveen.com/what-are-the-types-of-hashtags/ Watson, E. (2014). Emma Watson: Gender equality is your issue too. Retrieved January 4, 2015, from http://www.unwomen.org/en/news/stories/2014/9/emma-watson-gender-equality -is- your-issue-too Zappavigna, M. (2011). Ambient affiliation: A linguistic perspective on Twitter. New Media & Society, 13(5), 788-806. 16 Text Box http://dx.doi.org/10.3898/newf.78.02.2013 Text Box http://dx.doi.org/10.1177/1461444810385097 Introduction Data and Method Data Temporality and context Congruence and Divergence Theoretical Framework Distributional Results Discussion Engagement: collective identity Function and meaning Conclusion