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Fostering Civil Discourse Online: Linguistic Behavior in Comments of #MeToo Articles across Political Perspectives
Abstract
Linguistic style and affect shape how users perceive and assess political content on social media. Using linguistic methods to compare political discourse on far-left, mainstream and alt-right news articles covering the #MeToo movement, we reveal rhetorical similarities and differences in commenting behavior across the political spectrum. We employed natural language processing techniques and qualitative methods on a corpus of approximately 30,000 Facebook comments from three politically distinct news publishers. Our findings show that commenting behavior reflects how social movements are framed and understood within a particular political orientation. Surprisingly, these data reveal that the structural patterns of discourse among commenters from the two alternative news sites are similar in terms of their relationship to those from the mainstream - exhibiting polarization, generalization, and othering of perspectives in political conversation. These data have implications for understanding the possibility for civil discourse in online venues and the role of commenting behavior in polarizing media sources in undermining such discourse.
... These debating strategies are referred to as "discursive strategies" in our work. Previous works demonstrated that exploring linguistic practices could gain a profound understanding of how individuals engage in online discussions [22,102], present their arguments [114], and endeavor to influence others' perspectives and reflect identities [97]. Investigating discursive strategies within gender debate could facilitate understanding and communication across genders, nurture the development of constructive debating skills, and advance feminist advocacy [34,73,92]. ...
... In the United States, there are pivotal events that have played a role in empowering women's voices and addressing their daily needs. One of the most prominent movements is the #MeToo movement, which gained widespread attention and momentum in 2017 [4,5,47,51,97]. Through the use of social media platforms, women from diverse backgrounds shared their stories of sexual harassment and assault. However, it also faced criticism for the exclusion of women of color from feminist movements and the overrepresentation of white women [80,95]. ...
... For example, Baughan et al. examined how political identity, such as liberal and conservative accounts on Twitter, affects civility during political disagreements [11]. Rho et al. highlighted the presence of in-group and out-group dynamics among commenters on Breitbart (representing a right-wing viewpoint) and DemNow (representing a far-left viewpoint) regarding the #MeToo movement [97]. Breitbart commenters engage in derogatory language towards #MeToo participants reflecting out-group derogation, while Dem-Now commenters exhibit in-group favoritism based on race and socioeconomic factors. ...
... News articles on social media (hereby, referred to as news posts) are important spaces for online civil discourse around social issues [50,55]. Ideally, the inclusion of these political hashtags in articles headlines and news posts should foster heated, but constructive debate that generates a diversity of perspectives through discussion and greater interest in social issues. ...
... We know that people who use political hashtags are doing so to denote alignment with an issue [40], personalize expression of why a particular story is important [43], and encourage others to engage in the content of the news story [55]. We do not know how this practice is received and whether or not a general audience engages with hashtagged news content in a manner that is aligned with this intent. ...
... We also see "hashtag wars" when political hashtags are used as a form of expressing commentary and backlash against the initial issue and movement [26,34]. Therefore, political hashtags, especially in political outlets can be wielded as partisan tools that spur excessive controversy and division, thereby undermining conditions for civil discourse [55]. ...
Both hashtag activists and news organizations assume that trending political hashtags effectively capture the nowness of social issues that people care about [20]. In fact, news organizations with growing social media presence increasingly capitalize the use of political hashtags in article headlines and social media news post-a practice aimed to generate new readership through lightweight news consumption of content by linking a particular story to a broader topic [28]. However, response to political hashtags can be complicated as demonstrated with the events surrounding #MeToo and #BlackLivesMa"er. In fact, the semantic simplicity of political hashtags o#en belies the complexities around the question of who gets to participate [71], what intersectional identities are included or excluded from the hashtag [45], as well as how the meaning of the hashtag expands and dri#s [10] depending on the context through which it is expressed. Overtime, reports show increasing backlash [70, 73, 74] and polarization [21, 52, 66, 67, 70] against key issues embodied by political hashtags. In this vein, we assume that political hashtags affect how people make sense of and engage with media content. However, we do not know how the presence of political hashtags-signaling that a news story is related to a current social issue-influences the assumptions potential readers make about the social content of an article. In this work we conducted a randomized control experiment to examine how the presence of political hashtags (particularly the most prevalently used #MeToo and #BlackLivesMatter) in social media news posts shape reactions across a general audience (n=1979). Our findings show that compared to the control group, people shown news posts with political hashtags perceive the news topic as less socially important and are less motivated to know more about social issues related to the post. People also find the news more partisan and controversial when hashtags are included. In fact, negative perception associated with political hashtags (partisan bias & topic controversy) mediates people's motivation to further engage with the news content). High-intensity Facebook users and politically moderate participants perceive news with political hashtags as more partisan compared to posts excluding hashtags. There are also significant differences in discourse patterns between the hashtag and control groups around how politically moderate respondents engage with the news content in their comments.
... To investigate unwarranted consequences in relation to language use, previous research on sociolinguistics and computer-mediated communication on social media largely applies quantitative approaches. For example, studies analyze the language selection of bilingual Twitter users regarding social capital [8], examine the rhetoric of comments on political Facebook posts [16], or interpret lexical markers of minority stress experience in Lesbian, Gay, Bisexual, Transgender, Queer + (LGBTQ+) communities on Reddit [18]. Moreover, research on the relationship between algorithmic systems and communicative practices also focuses on language use to avoid justified consequences. ...
... In June and July 2022, we conducted 19 qualitative semi-structured interviews [10] with TikTok creators in the U.S. (15), U.K. (2), and Canada (2) who were aged 19 to 32 and self-reported as White (16), Black (1), Asian (1), and biracial (White and Black) (1); 73% identified as female. As of July 2022, participants had between 14k and 554.1k followers on TikTok, and had posted between 44 and 1.9k videos, for which they in total received between 67.4k and 35.7m likes (see Table 2). ...
... Using a social media hashtag to contribute to a public conversation about gender concerns is the most often described digital feminist practice. Hashtags mobilise people around an issue and amplify messages, thus facilitating conversations and even functioning as markers of social identity (Rho et al., 2018). The most thoroughly studied feminist hashtag is #MeToo, which has (Clark, 2016), #YesAllWomen (Thrift, 2014) and #IAmNotAfraidToSayIt (Lokot, 2019). ...
... Conversely, misogynistic memes on an online forum (Cockerill, 2019) or anti-women hate speech in the comments section of a news article (Rho et al., 2018) ...
... Following Rho et al. 's approach [13], we used word embeddings [11] to analyze the semantic context in which a concept under study is framed. Based on co-occurrence of terms, word embeddings create a reduced multi-dimensional representation of a corpus of text that allows assessing the semantic proximity among terms in a corpus. ...
... Based on co-occurrence of terms, word embeddings create a reduced multi-dimensional representation of a corpus of text that allows assessing the semantic proximity among terms in a corpus. Thus, analyzing the closest terms of a given word can reveal the context in which it is used [13]. ...
The Cambridge Analytica scandal triggered a conversation on Twitter about data practices and their implications. Our research proposes to leverage this conversation to extend the understanding of how information privacy is framed by users worldwide. We collected tweets about the scandal written in Spanish and English between April and July 2018. We created a word embedding to create a reduced multi-dimensional representation of the tweets in each language. For each embedding, we conducted open coding to characterize the semantic contexts of key concepts: "information", "privacy", "company" and "users" (and their Spanish translations). Through a comparative analysis, we found a broader emphasis on privacy-related words associated with companies in English. We also identified more terms related to data collection in English and fewer associated with security mechanisms, control, and risks. Our findings hint at the potential of cross-language comparisons of text to extend the understanding of worldwide differences in information privacy perspectives.
... Following Rho et al. 's approach [13], we used word embeddings [11] to analyze the semantic context in which a concept under study is framed. Based on co-occurrence of terms, word embeddings create a reduced multi-dimensional representation of a corpus of text that allows assessing the semantic proximity among terms in a corpus. ...
... Based on co-occurrence of terms, word embeddings create a reduced multi-dimensional representation of a corpus of text that allows assessing the semantic proximity among terms in a corpus. Thus, analyzing the closest terms of a given word can reveal the context in which it is used [13]. ...
The Cambridge Analytica scandal triggered a conversation on Twitter about data practices and their implications. Our research proposes to leverage this conversation to extend the understanding of how information privacy is framed by users worldwide. We collected tweets about the scandal written in Spanish and English between April and July 2018. We created a word embedding to create a reduced multi-dimensional representation of the tweets in each language. For each embedding, we conducted open coding to characterize the semantic contexts of key concepts: "information", "privacy", "company" and "users" (and their Spanish translations). Through a comparative analysis, we found a broader emphasis on privacy-related words associated with companies in English. We also identified more terms related to data collection in English and fewer associated with security mechanisms, control, and risks. Our findings hint at the potential of cross-language comparisons of text to extend the understanding of worldwide differences in information privacy perspectives.
... According to a field experiment by Broockman et al. (2016), brief conversations that encourage people to take the perspective of others actively can significantly and durably reduce prejudice toward marginalized groups, such as transgender people [14]. Empathetic language not only fosters more constructive dialogue in online communities but also leads to more perspective-taking among users [101,102]. However, RQ3 findings show that for empathy-based counterspeech, while participants' self-perceived effectiveness and satisfaction were higher compared to other counterspeech strategies, so was their difficulty in writing it. ...
This study investigates how online counterspeech, defined as direct responses to harmful online content with the intention of dissuading the perpetrator from further engaging in such behavior, is influenced by the match between a target of the hate speech and a counterspeech writer's identity. Using a sample of 458 English-speaking adults who responded to online hate speech posts covering race, gender, religion, sexual orientation, and disability status, our research reveals that the match between a hate post's topic and a counter-speaker's identity (topic-identity match, or TIM) shapes perceptions of hatefulness and experiences with counterspeech writing. Specifically, TIM significantly increases the perceived hatefulness of posts related to race and sexual orientation. TIM generally boosts counter-speakers' satisfaction and perceived effectiveness of their responses, and reduces the difficulty of crafting them, with an exception of gender-focused hate speech. In addition, counterspeech that displayed more empathy, was longer, had a more positive tone, and was associated with higher ratings of effectiveness and perceptions of hatefulness. Prior experience with, and openness to AI writing assistance tools like ChatGPT, correlate negatively with perceived difficulty in writing online counterspeech. Overall, this study contributes insights into linguistic and identity-related factors shaping counterspeech on social media. The findings inform the development of supportive technologies and moderation strategies for promoting effective responses to online hate.
... We use Kruskal-Wallis H test to analyze the differences among the scores for each message topic. The Kruskal-Wallis H test is also a non-parametric test commonly used to compare differences in more than two independent conditions (e.g., in HCI studies (e.g., [30,69])), especially when the data normality was violated, as confirmed in our cases. To compare the topics of conversations in the ComPeer and baseline group, we conduct unpaired T test and Mann-Whitney U test on the number of occurrences for each topic, based on their normality (attached in subsection A.8 in Appendix). ...
Conversational Agents (CAs) acting as peer supporters have been widely studied and demonstrated beneficial for people's mental health. However, previous peer support CAs either are user-initiated or follow predefined rules to initiate the conversations, which may discourage users to engage and build relationships with the CAs for long-term benefits. In this paper, we develop ComPeer, a generative CA that can proactively offer adaptive peer support to users. ComPeer leverages large language models to detect and reflect significant events in the dialogue, enabling it to strategically plan the timing and content of proactive care. In addition, ComPeer incorporates peer support strategies, conversation history, and its persona into the generative messages. Our one-week between-subjects study (N=24) demonstrates ComPeer's strength in providing peer support over time and boosting users' engagement compared to a baseline user-initiated CA.
... Especially considering decolonization's socio-psychological aspects [57,58], recovering cultural identity, understanding history, and examining colonial influence in regional geopolitics, economic hurdles, and social injustices become contributing factors to the decolonial discourse, which often takes shape and manifests through technology like online platforms [44,100]. Prior CSCW and social computing literature have highlighted how contemporary information and communication technologies (ICT) like online platforms can help marginalized communities-those who are pushed to the periphery of society, based on individual or multiple interconnected dimensions of identity [179], to raise their voices and represent diverse opinions [44,146,150]. Researchers have particularly explored how user-generated video-sharing online platforms support people in expressing and negotiating their cultural identities [36,126] and participating in sociopolitical discussions [13,16]. ...
Colonialism--the policies and practices wherein a foreign body imposes its ways of life on local communities--has historically impacted how collectives perceive themselves in relation to others. One way colonialism has impacted how people see themselves is through nationalism, where nationalism is often understood through shared language, culture, religion, and geopolitical borders. The way colonialism has shaped people's experiences with nationalism has shaped historical conflicts between members of different nation-states for a long time. While recent social computing research has studied how colonially marginalized people can engage in discourse to decolonize or re-imagine and reclaim themselves and their communities on their own terms--what is less understood is how technology can better support decolonial discourses in an effort to re-imagine nationalism. To understand this phenomenon, this research draws on a semi-structured interview study with YouTubers who make videos about culturally Bengali people whose lives were upended as a product of colonization and are now dispersed across Bangladesh, India, and Pakistan. This research seeks to understand people's motivations and strategies for engaging in video-mediated decolonial discourse in transnational contexts. We discuss how our work demonstrates the potential of the sociomateriality of decolonial discourse online and extends an invitation to foreground complexities of nationalism in social computing research.
... While there are several socio-economic and legal studies on text classification based on Natural Language Processing (NLP) and also some NLP-based analyses on language development, literature on text-based causal inference is scarce. The social science literature on text classification ranges from studies on differences in the linguistic style of posts in different online communities [4] and of comments on #MeToo articles in different news outlets [5] to studies that develop classifiers for political speeches in order to predict the speaker's ideology [6] or identify their sentiment towards the topic discussed in the speech [7]. In the legal domain, [8] developed a document classifier for judicial opinions from U.S. circuit courts that classifies the opinions according to the predicted ideological direction (conservative vs. liberal) of the decision. ...
This study assesses the effect of the #MeToo movement on the language used in judicial opinions on sexual violence related cases from 51 U.S. state and federal appellate courts. The study introduces various indicators to quantify the extent to which actors in courtrooms employ language that implicitly shifts responsibility away from the perpetrator and onto the victim. One indicator measures how frequently the victim is mentioned as the grammatical subject, as research in the field of psychology suggests that victims are assigned more blame the more often they are referred to as the grammatical subject. The other two indices designed to gauge the level of victim-blaming capture the sentiment of and the context in sentences referencing the perpetrator. Additionally, judicial opinions are transformed into bag-of-words and tf-idf vectors to facilitate the examination of the evolution of language over time. The causal effect of the #MeToo movement is estimated by means of a Difference-in-Differences approach comparing the development of the language in opinions on sexual offenses and other crimes against persons as well as a Panel Event Study approach. The results do not clearly identify a #MeToo-movement-induced change in the language in court but suggest that the movement may have accelerated the evolution of court language slightly, causing the effect to materialize with a significant time lag. Additionally, the study considers potential effect heterogeneity with respect to the judge’s gender and political affiliation. The study combines causal inference with text quantification methods that are commonly used for classification as well as with indicators that rely on sentiment analysis, word embedding models and grammatical tagging.
... Extreme discourses, such as the ones reproduced in the manosphere, are often blamed on the design of social media, which are claimed to lead to a polarization of extreme positions rather than a broader understanding of different perspectives (Rho et al., 2018). Moreover, while major social media services such as Facebook, Instagram, TikTok, and Twitter are largely curated by algorithms and scoring systems, there are platforms that avoid this type of design. ...
... Más tanulmányok az értelmezés támogatására alkalmaztak kvalitatív megközelítést. Rho et al. (2018) például diskurzuselemzést használt az összes olyan hozzászólás elemzésére, amely tartalmazza az előzetesen felderített legfontosabb kifejezéseket. Hasonlóképpen Grover et al. (2019b) a LIWC-elemzés által fontosnak talált kifejezéseket tartalmazó tweetek kvalitatív elemzését végezte el. ...
Bár a nyelv a társadalmi interakciók egy fontos eszköze, a kvantitatív társadalomkutatás – elsősorban adatgyűjtési és feldolgozási eszközök hiányában - mégsem használta igazán évtizedeken át. A helyzet az utóbbi évtizedben - a digitalis forradalomnak köszönhetően - gyökeresen megváltozott, a “text as data” mozgalom keretében a szöveges adat, mint empirikus társadalomkutatási bázis használata exponenciális ütemben terjed.
A szöveges adatok mennyiségének és hozzáférhetőségének ez a forradalma jelentősen kiszélesítette az empirikusan vizsgálható társadalomkutatási kérdések körét: az egyének, csoportok és intézmények viselkedését, azok kölcsönhatását és időbeli dinamikáját naponta többmillió terrabyte-nyi digitális szöveg képezi le, s ez az adatvagyon a digitalizáció előrehaladtával egyre sokszorozódik.
A társadalmat leíró szöveges adatok forradalmával párhuzamosan az utóbbi tíz évben a számítási kapacitások és azzal párhuzamosan az adatok elemzésére szolgáló szöveganalitikai technológiák robbanásszerű fejlődése is bekövetkezett, s az új technológiák a szöveg feldolgozásának már releváns mélységét nyújtják. Ez a robbanás a számítástudomány és számítógépes nyelvészet után a bölcsészet- és a társadalomtudományokat, így a szociológiát is elérte.
A kötet ezeket az inspiráló lehetőségeket mutatja be, a természetes nyelvfeldolgozás (natural language processing, NLP) szociológiai alkalmazásaiba engedve bepillantást, az ELTE Társadalomtudományi Karán a Research Center for Computational Social Science kutatócsoportban 2018 óta folyó kutatásokon, mint esettanulmányokon keresztül.
Az NLP technikai oldalának ismertetésére kiváló források állnak rendelkezésre, de a társadalomkutatási tapasztalatokat és kihívásokat kevesebb szerző tárgyalja. A társadalomkutatás alkalmazási specifikumát az adja, hogy az itt tárgyalt problémák egy évszázados kutatási paradigmába vannak ágyazva, kérdésfeltevései így lényegesen különböznek a számítástudomány vagy az ipari felhasználás kérdéseitől. Ennek a különbségnek pedig tudatában kell lennünk, amikor adaptáljuk az informatika oldaláról érkező innovációt.
A könyv ideális olvasója az a társadalomkutató, aki érdeklődik a számítógépes szövegelemzés lehetőségei iránt. Ugyanakkor a társadalom empirikus megismerésének tudományos módszerei iránt érdeklődő laikusoknak is ajánlható a könyv ismeretterjesztő munkaként, hiszen a szerző konkrét példákkal szemléltet és közérthetően magyaráz.
... Research on the MeToo movement specifically has highlighted the political polarization surrounding the movement (Blumell & Huemmer, 2019;Castle et al., 2020;Clark & Evans, 2019;Ha Rim Rho et al., 2018), including the importance of political ideology in shaping MeToo engagement and tweet content (Blumell & Huemmer, 2019;Castle et al., 2020). Politics and political affiliation shaped politicians' engagement with the movement (Clark & Evans 2019), as well as affecting which individuals experienced an increased awareness and concern about sexual violence as a result of MeToo. ...
#MeToo sought to combat sexual violence but evolved into a polarizing movement in the United States. Using a random sample of 5,153 tweets with #MeToo posted between 2017 and 2019 to explore the language and themes individuals use to polarize conversations around sexual violence, we find that MeToo supporters used rights-and-justice-focused language to advocate for survivors. In contrast, MeToo detractors employed legal and violent language to victimize the alleged perpetrators and villainize victims of sexual violence and their supporters. This demonstrates how “linguistic hijacking” unfolded online, with movement opponents co-opting key terms (like “victim”) to undermine movement supporters’ goals.
... For example, prior studies build classification models to contextualize out-of-vocabulary terms on Twitter (Maity et al., 2016), analyze language selection regarding social capital of bilingual Twitter users (S. Kim et al., 2014), examine language switching as user strategy in search engine usage (Wang & Komlodi, 2018), or perform rhetorical analysis of commenting on political Facebook posts to understand users' perception of partisan messages (Rho et al., 2018). Some mixed-method studies analyze manipulative rhetorics in factoid online articles (Tian et al., 2022), or interpret lexical markers of minority stress experience in LGBTQ+ communities on Reddit (Saha et al., 2019). ...
Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent
words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.
... Different platforms play distinct roles in information diffusion (Park et al., 2015), with the clustering of ideas on one platform sparking connective action on another (Pearce et al., 2020). In the context of #MeToo, Twitter discourse focused on empathy and support for sexual violence victims, serving as a disseminator of content on forums like Reddit (Manikonda, 2018), whereas Facebook users mostly reacted to news articles about #MeToo (Rho et al., 2018). On Instagram, #MeToo was employed as a commodifying strategy by businesses and brands (Afnan et al., 2019). ...
... In our empirical work, we have focused on first-hand reports obtained through semi-structured interviews with the co-founders, and survey data from more peripheral participants on the Instagram page. This is different from other HCI and CSCW studies of hashtag activism and networked #MeToo activism, which often use content scraping and analysis of social media posts and comments [e.g., 29,63,72]. Instead, this paper seeks to nuance the kinds of labor underlying the orchestration and personal engagement at the core of #MeToo initiatives. We thereby expand a body of work that has described the role of design in enabling polarized discussions [73], fostering anonymity [4,66], or enabling networked activism [29], and the role of trust in sharing stories and developing relationships between researchers and participants [38]. ...
This paper extends the literature on social media activism by foregrounding the invisible work of orchestrating it. We analyze the activities of a #MeToo group in a Northern European country and characterize the group's efforts to catalog incidents of gender-based harassment and discrimination as an activist, platform-mediated participatory writing project. Our analysis details the work of organizing this form of activism, particularly: 1) the editorial work that underlies publishing personal stories on social media, 2) the emotional labor that unfolds as part of this form of writing, and 3) the work of creating publics. By drawing attention to these efforts, the paper frames activism as effortfully driven, sometimes in tension with the platform or evolving value positions. We conclude with a discussion on the role of social networking sites in organizing activism where writing is central, and with a set of sensitivities that can support designers and activists alike in designing socio-technical practices concerned with social change.
... There is also a lot of interest in the CSCW community around the news and semantic analysis, including but not limited to new datasets, models to analyze news headings for misinformation or polarization, analysis of reactions to news stories, and methods to help journalists write [14,57,58,60]. For example, Liu et al. [45] proposes a computer-aided solution to combat extreme political polarization by reversing or neutralizing the political polarity of news headlines and articles. ...
Increasing the number of cyclists, whether for general transport or recreation, can provide health improvements and reduce the environmental impact of vehicular transportation. However, the public's perception of cycling may be driven by the ideologies and reporting standards of news agencies. For instance, people may identify cyclists on the road as "dangerous" if news agencies overly report cycling accidents, limiting the number of people that cycle for transportation. Moreover, if fewer people cycle, there may be less funding from the government to invest in safe infrastructure. In this paper, we explore the perceived perception of cyclists within news headlines. To accomplish this, we introduce a new dataset, "Bike Frames", that can help provide insight into how headlines portray cyclists and help detect accident-related headlines. Next, we introduce a multi-task (MT) regularization approach that increases the detection accuracy of accident-related posts, demonstrating improvements over traditional MT frameworks. Finally, we compare and contrast the perceptions of cyclists with motorcyclist-related headlines to ground the findings with another related activity for both male- and female-related posts. Our findings show that general news websites are more likely to report accidents about cyclists than other events. Moreover, cyclist-specific websites are more likely to report about accidents than motorcycling-specific websites, even though there is more potential danger for motorcyclists. Finally, we show substantial differences in the reporting about male vs. female-related persons, e.g., more male-related cyclists headlines are related to accidents, but more female-related motorcycling headlines about accidents. WARNING: This paper contains descriptions of accidents and death.
... Other studies applied a qualitative approach to support interpretation: Rho et al. [89] for example used discourse analysis to analyze all comments that contain the top relevant terms computationally detected beforehand. Similarly, Grover et al. [30] carried out a qualitative analysis of tweets containing terms that were found to be important by LIWC analysis. ...
Unlabelled:
As part of the "text-as-data" movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 (n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research.
Supplementary information:
The online version contains supplementary material available at 10.1007/s42001-022-00196-2.
... While there are several socio-economic and legal studies on text classification based on Natural Language Processing (NLP) and also some NLP-based analyses on language development, literature on text-based causal inference is scarce. The social science literature on text classification ranges from studies on differences in the linguistic style between posts in different online communities (Khalid and Srinivasan (2020)) and between comments on #MeToo articles in different news outlets (Rho, Mark, and Mazmanian (2018)) to studies that develop classifiers for political speeches in order to predict the speaker's ideology (Yu, Kaufmann, and Diermeier (2008)) or identify his/her sentiment towards the topic discussed in the speech (Abercrombie and Batista-Navarro (2018)). In the legal domain, Hausladen, Schubert, and Ash (2020) developed a document classifier for judicial opinions from U.S. circuit courts that classifies the opinions according to the predicted ideological direction (conservative vs. liberal) of the decision. ...
This study assesses the effect of the #MeToo movement on different quantifiers of the 2015-2020 judicial opinions in sexual violence related cases from 51 U.S. courts. The judicial opinions are vectorized into bag-of-words and tf-idf vectors in order to study their development over time. Further, different indicators quantify to what extent the judges use a language that implicitly shifts some blame from the victim(s) to the perpetrator(s). These indicators measure how the grammatical structure, the sentiment and the context of sentences mentioning the victim(s) and/or perpetrator(s) change over time. The causal effect of the #MeToo movement is estimated by means of Difference-in-Differences comparing the development of the language in opinions on sexual violence and other interpersonal crime related cases as well as a Panel Event Study approach. The results do not clearly identify a #MeToo-movement-induced change in the language in court but suggest that the movement may have accelerated the evolution of court language slightly, causing the effect to materialize with a significant time lag. Additionally, the study considers potential effect heterogeneity with respect to the judge's gender and his/her political affiliation. The study combines causal inference with text quantification methods that are commonly used for classification as well as with indicators from the fields of sentiment analysis, word embedding models and grammatical tagging.
... Furthermore, Rho et al. [62] investigated the linguistic structure employed by people commenting on the #metoo online social movement. Results from their research revealed that the rhetorical style of respondents predicted their political leaning, and by extension, how they might respond to other related discourses. ...
Designers' use of deceptive and manipulative design practices have become increasingly ubiquitous, impacting users' ability to make choices that respect their agency and autonomy. These practices have been popularly defined through the term "dark patterns" which has gained attention from designers, privacy scholars, and more recently, even legal scholars and regulators. The increased interest in the term and underpinnings of dark patterns across a range of sociotechnical practitioners intrigued us to study the evolution of the concept, to potentially speculate the future trajectory of conversations around dark patterns. In this paper, we examine the history and evolution of the Twitter discourse through #darkpatterns from its inception in June 2010 until April 2021, using a combination of quantitative and qualitative methods to describe how this discourse has changed over time. We frame the evolution of this discourse as an emergent transdisciplinary conversation that connects multiple disciplinary perspectives through the shared concept of dark patterns, whereby these participants engage in a conversation marked by socio-technical angst in order to identify and fight back against deceptive design practices. We discuss the potential future trajectories of this discourse and opportunities for further scholarship at the intersection of design, policy, and activism.
... A large-scale analysis of user discussion conducted by Ha Rim Rho et al. (2018) outlined how readers of different online news outlets constructed competing narratives around the #Metoo campaign. The authors filtered around 30,000 user comments from the Facebook API and analyzed the content and form of interactions based on machine learning algorithms. ...
Social media has become a major platform for information-exchange, discourse, and protest and has been linked to a wide range of pressing macro developments. Consequenlty, there is significant interest from scholars as well as from the wider publuc to understand how social media affordances interact with human behavior. In attempts to address these demands, the present article borrows from the social identity tradition to explain group formation processes in Web 2.0 and other online ecosystems. We propose that online users creatively and strategically exploit the affordances provided by platforms and technologies to construct and perform collective selfhood. We emphasize the relevance of community development, norm consensualization, and emotional alignment as recursive dynamic processes that – in symbiosis – provide a functional basis for social identities. We outline these proposed mechanisms based on a corpus of interdisciplinary literature and suggest avenues for future research.
... Insights gained through our work can be used by social movement scholars to identify better quality Twitter data for their research. Finally, social movement scholars also have been using various approaches to mine data from other online platforms [66,67], and are likely facing similar issues due to the lack of benchmarking. The evaluation process proposed in our study can be adopted and revised for benchmark analysis on other platforms. ...
Political and social scientists have been relying extensively on keywords such as hashtags to mine social movement data from social media sites, particularly Twitter. Yet, prior work demonstrates that unrepresentative keyword sets can lead to flawed research conclusions. Numerous keyword expansion methods have been proposed to increase the comprehensiveness of keywords, but systematic evaluations of these methods have been lacking. Our paper fills this gap. We evaluate five diverse keyword expansion techniques (or pipelines) on five representative social movements across two distinct activity levels. Our results guide researchers who aim to use social media keyword searches to mine data. For instance, we show that word embedding-based methods significantly outperform other even more complex and newer approaches when movements are in normal activity periods. These methods are also less computationally intensive. More importantly, we also observe that no single pipeline can identify little more than half of all movement-related tweets when these movements are at their peak mobilization period offline. However, coverage can increase significantly when more than one pipeline is used. This is true even when the pipelines are selected at random.
... Similarly, although we do not demonstrate it here, we could subset a corpus by variables other than time, and train separate embedding models on each subset in order to measure how the meaning of terms varies across, e.g., individuals, communities, or An et al. 2019;Rho, Mark, and Mazmanian 2018;Schild et al. 2020) . In both cases, just like the time-lapse approach where the embedding space is allowed to vary over time, here the embedding space is allowed to vary by authors-either individuals or collectives. ...
Using the presence or frequency of keywords is a classic approach in the formal analysis of text, but has the drawback of glossing over the relationality of word meanings. Word embedding models overcome this problem by constructing a standardized meaning space where words are assigned a location based on relations of similarity to, and difference from, other words based on how they are used in natural language samples. We show how word embeddings can be put to the task of interpretation via two kinds of navigation. First, one can hold terms constant and measure how the embedding space moves around them—much like astronomers measured the changing of celestial bodies with the seasons. Second, one can also hold the embedding space constant and see how documents or authors move relative to it—just as ships use the stars on a given night to determine their location. Using the empirical case of immigration discourse in the United States, we demonstrate the merits of these two broad strategies to advance formal approaches to cultural analysis.
... Analyzing the large quantity of behavioral data in diet-related posts on social media could be the first step in designing a support system for social media users. Previous studies in the field of human-computer interaction (HCI) have analyzed the users' behaviors through the utilization of data-driven technologies [6][7][8][9][10]. These studies analyze various user-created contents such as texts, images, or other categories of user logs [11][12][13]. ...
Background
Emotional eating (EE) is one of the most significant symptoms of various eating disorders. It has been difficult to collect a large amount of behavioral data on EE; therefore, only partial studies of this symptom have been conducted. To provide adequate support for online social media users with symptoms of EE, we must understand their behavior patterns to design a sophisticated personalized support system (PSS).
Objective
This study aimed to analyze the behavior patterns of emotional eaters as the first step to designing a personalized intervention system.
Methods
The machine learning (ML) framework and Latent Dirichlet Allocation (LDA) topic modeling tool were used to collect and analyze behavioral data on EE. Data from a subcommunity of Reddit, /r/loseit, were analyzed. This dataset included all posts and feedback from July 2014 to May 2018, comprising 185,950 posts and 3,528,107 comments. In addition, deleted and improperly collected data were eliminated. Stochastic gradient descent–based ML classifier with an accuracy of 90.64% was developed to collect refined behavioral data of online users with EE behaviors. The expert group that labeled the dataset to train the ML classifiers included a medical doctor specializing in EE diagnosis and a nutritionist with profound knowledge of EE behavior. The experts labeled 5126 posts as EE (coded as 1) or others (coded as 0). Finally, the topic modeling process was conducted with LDA.
ResultsThe following 4 macroperspective topics of online EE behaviors were identified through linguistic evidence regarding each topic: addressing feelings, sharing physical changes, sharing and asking for dietary information, and sharing dietary strategies. The 5 main topics of feedback were dietary information, compliments, consolation, automatic bot feedback, and health information. The feedback topic distribution significantly differed depending on the type of EE behavior (overall P
... Thus, a more focused question would be, can we leverage bots in online social space to positively in uence perceived social norms, which would then make people less prejudiced toward other religious groups? A body of CSCW/HCI research has explored the impact of perceived norms on shaping behavior [12,39,45], and thus the potential of bots for positive behavior change is certainly worth investigating in future studies. ...
Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.
... In Study 1, I used the #MeToo movement as the topical lens to compare how linguistic patterns manifest across three politically distinct (far-left, center, and alt-right) news publishers on Facebook [6]. The goal of this study is to investigate how linguistic attributes and rhetorical patterns of discourse differ among people who produce and consume different political news on social media. ...
Whether through television, newspapers, or more increasingly through Social Networking Sites (SNS), journalistic coverage of current events have long played a significant role in mediating knowledge and information to the public. The platforms and channels through which news is produced and consumed shape how the public talk about current issues, exemplifying the critical link between democratic discourse and the press. However, with the advent of social media, the display of online news content has increasingly changed over the years. This implies that the conditions and avenues through which audiences make sense of mediated politics through news have possibly changed as well. This is the premise that motivates my work. In my dissertation, I examine how social media news consumption impacts the viability of online political deliberation around news content. Specifically, I investigate how civil discourse is shaped in relation to political hashtags in the headlines and texts of social media news posts. I use both qualitative and computational (natural language processing) methods on publicly available social media news comments and survey data collected through large-scale experiments.
... Thus, a more focused question would be, can we leverage bots in online social space to positively influence perceived social norms, which would then make people less prejudiced toward other religious groups? A body of CSCW/HCI research has explored the impact of perceived norms on shaping behavior [11,38,44], and thus the potential of bots for positive behavior change is certainly worth investigating in future studies. ...
Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.
... In a people-centric approach, we analyze narratives that include individuals directly or indirectly involved in the movement: victims, perpetrators, influential commenters, reporters, etc. Unlike prior work focused on social media (Ribeiro et al. 2018;Rho, Mark, and Mazmanian 2018), our work examines the prominent role that more traditional outlets and journalists continue to have in the modern-era online media landscape. ...
In October 2017, numerous women accused producer Harvey Weinstein of sexual harassment. Their stories encouraged other women to voice allegations of sexual harassment against many high profile men, including politicians, actors, and producers. These events are broadly referred to as the #MeToo movement, named for the use of the hashtag "#metoo" on social media platforms like Twitter and Facebook. The movement has widely been referred to as "empowering" because it has amplified the voices of previously unheard women over those of traditionally powerful men. In this work, we investigate dynamics of sentiment, power and agency in online media coverage of these events. Using a corpus of online media articles about the #MeToo movement, we present a contextual affective analysis---an entity-centric approach that uses contextualized lexicons to examine how people are portrayed in media articles. We show that while these articles are sympathetic towards women who have experienced sexual harassment, they consistently present men as most powerful, even after sexual assault allegations. While we focus on media coverage of the #MeToo movement, our method for contextual affective analysis readily generalizes to other domains.
Colonialism--the policies and practices wherein a foreign body imposes its ways of life on local communities--has historically impacted how collectives perceive themselves in relation to others. One way colonialism has impacted how people see themselves is through nationalism, where nationalism is often understood through shared language, culture, religion, and geopolitical borders. The way colonialism has shaped people's experiences with nationalism has shaped historical conflicts between members of different nation-states for a long time. While recent social computing research has studied how colonially marginalized people can engage in discourse to decolonize or re-imagine and reclaim themselves and their communities on their own terms--what is less understood is how technology can better support decolonial discourses in an effort to re-imagine nationalism. To understand this phenomenon, this research draws on a semi-structured interview study with YouTubers who make videos about culturally Bengali people whose lives were upended as a product of colonization and are now dispersed across Bangladesh, India, and Pakistan. This research seeks to understand people's motivations and strategies for engaging in video-mediated decolonial discourse in transnational contexts. We discuss how our work demonstrates the potential of the sociomateriality of decolonial discourse online and extends an invitation to foreground complexities of nationalism in social computing research.
Meanspo, an antagonistic form of online support within the eating disorder (ED) community, involves the direct solicitation or sharing of aggressive and insulting online content. This study presents findings from a comprehensive qualitative analysis of #meanspo content on X (previously Twitter ) from May 2020 (N=752). Our analysis of tweets reveals that posts tagged with #meanspo can be of various natures. While commonly associated with extremely derogatory ED content, more than 80% of posts with the meanspo tag on X were non-aggressive. The study also explores potential inconsistencies in voluntary and involuntary meanspo specific content moderation, prompting inquiries into X's regulatory policies against such content and the distinct online self-presentation strategies employed by community members. Future contextual research is needed to understand the evolving nature of this social phenomenon and its potential clinical impacts on users over time, particularly concerning the unhealthy adoption of such content. TRIGGER WARNING: Explicit language & potentially triggering content.
Large-scale online platforms powered by user-generated content are extensively researched as venues of learning and knowledge production. In this ethnographically oriented study, we examine knowledge practices on a community question answering platform for computer programmers in relation to the platform mechanics of voting. Grounded in the practice theoretical perspective and drawing on the analysis of online discussion threads and platform-related online materials, our study unpacks the dominant practice of crowd-based curation, the complementing practice of distributed moderation, and the more marginal practice of providing feedback to content producers. The practices co-exist in tension and consonance, which are embedded in the materiality of the platform and are continuously enacted through user discursive boundary work, sustaining the mentioned practices as intelligible for other users, and outlining what counts as legitimately participation on the platform. The study contributes to existing research on the roles voting plays on online platforms, as well as offers implications for research on social and material organization of users' online practices. The study also discusses that it is the ambiguity around the mechanics of voting that allows practices to co-exist. While this ambiguity is often discussed by users as problematic, we suggest as potential implication of our study that it may be productive to design platforms for workable forms of ambiguity allowing knowledge practices to co-exist in tension and to provide space for user negotiations of these practices.
In this work, we examine the linguistic signature of online racial microaggressions (acts) and how it differs from that of personal narratives recalling experiences of such aggressions (recalls) by Black social media users. We manually curate and annotate a corpus of acts and recalls fromin-the-wild social media discussions, and verify labels with Black workshop participants. We leverage Natural Language Processing (NLP) and qualitative analysis on this data to classify (RQ1), interpret (RQ2), and characterize (RQ3) the language underlying acts and recalls of racial microaggressions in the context of racism in the U.S. Our findings show that neural language models (LMs) can classify acts and recalls with high accuracy (RQ1) with contextual words revealing themes that associate Blacks with objects that reify negative stereotypes (RQ2). Furthermore, overlapping linguistic signatures between acts and recalls serve functionally different purposes (RQ3), providing broader implications to the current challenges in content moderation systems on social media.
Open Source Software (OSS) development has attracted numerous developers. As a typical complex sociotechnical system, an OSS project often forms a hierarchical social structure where a few developers are elite while the rest are non-elite. Differences in social status may result in distinct language use behaviors in interpersonal communication. Characterizing such behaviors is critical for supporting efficient and effective communication among developers with different social statuses. This study empirically compared elite and non-elite developers' language behaviors in their communication. We compiled a corpus of - 216,000 discourses collected from 20 large projects on GitHub. We investigated the linguistic differences in three aspects, namely, linguistic styles and characters, main concerns, and sentence patterns. Our findings reveal that elite and non-elite developers showed different linguistic patterns and had different concerns in their discourses. Their discourses also reflect the variation of the main focuses in the development process. Furthermore, elite and non-elite developers exhibited noticeable patterns in their linguistic behaviors in accordance with their roles and corresponding divisions of labor in the production process, no matter which semantic contexts. These findings provide implications for supporting communication that crosses social statuses in OSS development.
The events surrounding the status of Alyssa Milano and #MeToo in 2017 strengthened the interest of scholars in the formation of online discourses about sexual violence and harassment. Drawing on the past scholarship, this case study
focuses on later events in the summer of 2020 and the consequent online discussions in Slovakia. Through discursive argumentative analysis, we discuss the formation of dichotomies of individual/personal vs. structural/political, the understanding of which is key to shaping feminist advocacy against violence and harassment.
In an era of intense partisanship, there is widespread concern that people are self-sorting into separate online communities which are detached from one another. Referred to as echo chambers, the phenomenon is sometimes attributed to the new media landscape and internet ecosystem. Of particular concern is the idea that communication between disparate groups is breaking down due to a lack of a shared reality. In this article, we look to evaluate these assumptions. Applying text and semantic network analyses, we study the language of users who represent distinct partisan political ideologies on Reddit and their discussions in light of the January 6, 2021, Capitol Riots. By analyzing over 58k posts and 3.4 million comments across three subreddits, r/politics, r/democrats, and r/Republican, we explore how these distinct groups discuss political events to understand the possibility of bridging across echo chambers. The findings of this research study provide insight into how members of distinct online groups interpret major political events.
We investigate perceptions of tweets marked with the #BlackLivesMatter and #AllLivesMatter hashtags, as well as how the presence or absence of those hashtags changed the meaning and subsequent interpretation of tweets in U.S. participants. We found a strong effect of partisanship on perceptions of the tweets, such that participants on the political left were more likely to view #AllLivesMatter tweets as racist and offensive, while participants on the political right were more likely to view #BlackLivesMatter tweets as racist and offensive. Moreover, we found that political identity explained evaluation results far better than other measured demographics. Additionally, to assess the influence of hashtags themselves, we removed them from tweets in which they originally appeared and added them to selected neutral tweets. Our results have implications for our understanding of how social identity, and particularly political identity, shapes how individuals perceive and engage with the world.
Background
This paper argues that public addresses of heads of state are critical for public health policy during pandemics. Drawing insights from studies that investigate metaphors and frames in political and public health discourses, it explores how the Philippine government, especially President Rodrigo Duterte, framed COVID-19. In doing so, it hopes to broaden the understanding of how political rhetoric may be constitutive of public health policy.
Methods
The analysis of the public addresses entailed three interrelated levels: (1) a description of broad historical, social, political, and cultural contexts of public addresses under investigation, (2) an explanation of the communicative situation including the production and consumption of these addresses—processes that mediate between the text and context, and (3) a textual analysis, which substantiates how the discursive patterns are realized through the president’s rhetorical choices.
Results
Our analysis reveals that the president consistently deployed the rhetorical strategies of (1) enemization, (2) legitimization of the incumbent administration, and (3) dismissal of critics. The configuration of these strategies sustains a binaristic discourse structure that lays blame on a political other while the government asserts its legitimacy during a public health crisis.
Conclusions
These rhetorical strategies organize support for public health policy by a populist administration to manage COVID-19. Implications of political rhetoric to public health and risk communication are discussed.
While there is increasing global attention to data privacy, most of their current theoretical understanding is based on research conducted in a few countries. Prior work argues that people's cultural backgrounds might shape their privacy concerns; thus, we could expect people from different world regions to conceptualize them in diverse ways. We collected and analyzed a large-scale dataset of tweets about the #CambridgeAnalytica scandal in Spanish and English to start exploring this hypothesis. We employed word embeddings and qualitative analysis to identify which information privacy concerns are present and characterize language and regional differences in emphasis on these concerns. Our results suggest that related concepts, such as regulations, can be added to current information privacy frameworks. We also observe a greater emphasis on data collection in English than in Spanish. Additionally, data from North America exhibits a narrower focus on awareness compared to other regions under study. Our results call for more diverse sources of data and nuanced analysis of data privacy concerns around the globe.
Facilitated by the widespread use of the communication tool #MeToo, the online public space has been opening up to survivor stories and is providing interpersonal emotional support to individuals with experiences of sexual harassment or violence. Witnessing the widespread and collective demand for de-stigmatised survivor-centred and empowering approaches to violence, the scholarly community has been discussing these events as the example of (feminist) counter-publics with counter-narratives, or as the “transformative politics of visibility”. This case study contemplates the reactionary forces, focusing on negotiating discursive practices which aim to resist (feminist) counter-narratives in the Slovak online environment. We wish to enrich the existing literature by drawing on the developing scholarship of discourse analysis studies in the “#MeToo era” and by looking at the argumentative strategies applied by the discussants with regards to one (potential) case of sexual harassment. We do so by proposing to treat the interpretative frameworks of the discussants as stemming from experiences with dominant media narratives, and as being built around the discursive negotiations of “public-private space” and “personalpolitical issues”, well-known to feminist theorisations of sexual harassment and violence since the 1970s.
Online multiplayer games like Minecraft, gaining increasing popularity among present-day youth, include rich contexts for social interactions but are also rife with interpersonal conflict among players. Research shows that a variety of socio-technical mechanisms (e.g., server rules, chat filters, use of in-game controls to ban players, etc.) aim to limit and/or eliminate social conflict in games like Minecraft. However, avoiding social conflict need not necessarily always be a useful approach. Broadly defined in CSCW literature as a phenomenon that may arise even amidst mutual cooperation, social conflict can yield positive outcomes depending on how it is managed (e.g., [Easterbrook et al.,1993]). In fact, the aforementioned approaches to avoid conflict may not be helpful as they do not help youth understand how to address similar interpersonal differences that may occur in other social settings. Furthermore, prior research has established the value of developing conflict-resolution skills during early adolescence within safe settings, such as school/after-school wellness and prevention interventions (e.g.,[Shure, 1982], [Aber et al., 1998]), for later success in any given interpersonal relationship. While games like Minecraft offer authentic contexts for encountering social conflict, little work thus far has explored how to help youth develop conflict-resolution skills by design interventions within online interest-driven settings. Drawing from prior literature in CSCW, youth wellness and prevention programs, we translated offline evidence-based strategies into the design of an online, after-school program that was run within a moderated Minecraft server. The online program, titled Survival Lab, was designed to promote problem-solving and conflict-resolution skills in youth (ages 8-14 years). We conducted a field study for six months (30 youth participants, four college-age moderators, and one high-school volunteer aged 15 years) using in-game observations and digital trace ethnographic approaches. Our study data reveals that participating youth created community norms and developed insightful solutions to conflicts in Survival Lab. Our research offers three key takeaways. Firstly, online social games like Minecraft lend themselves as feasible settings for the translation of offline evidence-based design strategies in promoting the development of conflict-resolution and other social competencies among youth. Secondly, the design features that support structured and unstructured play while enabling freedom of choice for youth to engage as teams and/or individuals are viable for collective or community-level outcomes. Third and finally, moderators, as caring adults and near-peer mentors, play a vital role in facilitating the development of conflict-resolution skills and interest-driven learning among youth. We discuss the implications of our research for translating offline design to online play-based settings as sites and conclude with recommendations for future work.
Understanding ideological conflict has been a topic of interest in CSCW, for example in Value Sensitive Design research. More specifically, understanding ideological conflict is important for studying social media platforms like Twitter, which provide the ability for people to freely express their thoughts and opinions on contentious political events. In this work, we examine Twitter data to understand the moral, affective, and cognitive differences in language use between two opposing sides of the political debate over immigration related issues in the United States in the year since the 2016 presidential election. In total, we analyzed and compared the language of 45,045 pro-immigration tweets and 11,213 anti-immigration tweets spread across this period. Based on Moral Foundations Theory used to understand ideological conflict, we found pro-immigration tweets to contain more language associated with moral foundations of harm, fairness, and loyalty. Anti-immigration tweets contained more language associated with moral foundations of authority, more words associated with cognitive rigidity and more 3rd person pronouns and negative emotion. We discuss the implications of our research for political communication over social media, and for incorporating Moral Foundations Theory into other CSCW research. We discuss the potential application of this theory for Value Sensitive Design research.
With the maturation of social media as a form of communication and the decline in mainstream institutional journalism, scholars must reevaluate the processes through which information is produced, distributed, assimilated, and acted upon. We consider five important, new, digitally enabled “pump-valves” in the flow of socio-political information and frames: platforms, analytics, algorithms, ideological media, and rogue actors. We revise the cascading network activation model of frame activation and spread, developed before digital media’s rise. This new integrated model illuminates how these five features of digitalization affect relationships among elites, traditional media, and individuals, and suggests research on whether they flatten and democratize hierarchies of information control and power or entrench dominant structures.
Immigrants, as a group, are frequently described in ways, such as vermin or disease, that portray them as less than human. This type of dehumanizing language leads to negative emotional responses and negative attitudes toward the dehumanized group. This paper examines how the dehumanization of immigrants influences immigration policy attitudes. I use original experimental data to show that dehumanization leads to more negative immigration attitudes. I further find that these negative attitudes are mediated by the role of emotion. Dehumanization increases anger and disgust toward immigrants, which causes anti-immigrant sentiment.
In this research, we examine the advocacy and community building of transgender women on Twitter through methods of network and discourse analysis and the theory of networked counterpublics. By highlighting the network structure and discursive meaning making of the #GirlsLikeUs network, we argue that the digital labor of trans women, especially trans women of color, represents the vanguard of struggles over self-definition. We find that trans women on Twitter, led by Janet Mock and Laverne Cox, and in response to histories of misrepresentation and ongoing marginalization and violence, deliberately curate an intersectional networked counterpublic that works to legitimize and support trans identities and advocate for trans autonomy in larger publics and counterpublics.
Social media have become central places where public discourses are generated, sustained, and circulated around public events. So far, much research has examined large-scale dissemination patterns of prominent statements, opinions, and slogans circulated on social media, such as the analysis of keywords and hashtags on Twitter regarding a political event. However, little attention has been paid to understanding how local socio-cultural-political conditions influence the formation and development of public discourses on social media. To explore this question, we analyzed public discourses about Hong Kong's Umbrella Movement on two distinct social media sites, Facebook and Weibo, the largest micro-blogging service in China. Facebook topped Hong Kong citizens' usage of social media sites, while Weibo's primary user base is mainland Chinese. The social movement and these two social media sites provide a unique opportunity to explore the commonalities and differences between social media discourses generated by two different cultures. Using grounded theory and discourse analysis, we reveal how people on two sites reasoned about the many incidents of the movement and developed sometimes similar but other times strikingly different discourses. We trace the links between different discourses and the socio-cultural-political conditions of Hong Kong and mainland China. We discuss how this study may contribute deeper understandings of public discourses on social media to the CSCW literature.
How do the words we use to talk about politics influence political attitudes and evaluations? I focus specifically on negative affective language—words which individuals have preexisting negative reactions toward. Considering the Affect Infusion Model (AIM), processing style influences how individuals use affect when making decisions. The impact of affective language depends upon the complexity of the decision. In simpler processing tasks, individuals will use affect as a heuristic. This causes a misattribution of generalized negative affect onto a political target, leading to harsher evaluations. When a decision is complex, affective language influences how new information is stored in memory, along with improving information recall and abstract thinking. For those who are exposed to negative affective language, negative evaluations of politicians persist more strongly in memory, while these evaluations fade away when affect is used as a heuristic.
In this paper, we examine stigma related to class identity online through an empirical examination of Elite University Class Confessions (EUCC). EUCC is an online space that includes a Facebook page and a surrounding sociotechnical ecosystem. It is a community of, for, and about low-income and first generation students at an elite university. By bringing in a community that learns and engages with users' socioeconomic struggles, EUCC engenders unique restorative properties for students experiencing class stigma. EUCC's restorative properties foster new ways of understanding one's stigmatized identity through meaning-making interactions in a networked sociotechnical system. We discuss how EUCC's design shapes the nature of user interactions around class stigma, and explore in depth how people experience stigma differently through the restorative properties of EUCC.
Humans strive for balance between their motivations for accurate judgments and their cognitive effort. Although heuristic cues provide cognitive shortcuts, heuristic-based processing does not guarantee quality judgments. Based on the heuristic-systematic model, this study investigates if social media users select cues to use for their judgments based on cue applicability and reliability, which can facilitate more effective heuristic processing.
Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabrications aim to instill a false sense of truth in the reader, a successful satirical hoax must eventually be exposed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and illustrating the unique features of satirical news, which mimics the format and style of journalistic reporting. Satirical news stories were carefully matched and examined in contrast with their legitimate news counterparts in 12 contemporary news topics in 4 domains (civics, science, business, and “soft” news). Building on previous work in satire detection, we proposed an SVM-based algorithm, enriched with 5 predictive features (Absurdity, Humor, Grammar, Negative Affect, and Punctuation) and tested their combinations on 360 news articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects satirical news with a 90% precision and 84% recall (F-score=87%). Our work in algorithmically identifying satirical news pieces can aid in minimizing the potential deceptive impact of satire. [Note: The associated dataset of the Satirical and Legitimate News, S-n-L News DB 2015-2016, is available via http://victoriarubin.fims.uwo.ca/news-verification/ . The set is password-protected to avoid automated harvesting. Please feel free to request the password, if you are interested.]
This article demonstrates the ways in which youth of color played an active role in debates that erupted on Twitter following the tragic deaths of Michael Brown and Eric Garner in 2014. These debates on social media represent a larger struggle over discourse on race and racism across the nation. Drawing from critical theory and race theory, and engaging in the relatively new practice of using Twitter as a source of data for sociological analysis, this article examines Twitter as an emerging public sphere and studies the hashtags “#AllLivesMatter” and “#BlackLivesMatter” as contested signs that represent dominant ideologies. This article consists of a qualitative textual analysis of a selection of Twitter posts from December 3 to 7, 2014, following the nonindictments of officers in the murders of Michael Brown and Eric Garner. The debates on Twitter reveal various strategies that youth of color employed to shape the national discourse about race in the wake of these high-profile tragedies.
This research surveys the current state-of-the-art technologies that are instrumental in the adoption and development of fake news detection. " Fake news detection " is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres. The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories – linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network-based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system.
In the last decade the framing perspective has gained increasing popularity among social movement researchers and theorists. Surprisingly, there has been no critical assessment of this growing body of literature. Though the perspective has made significant contributions to the movements literature, it suffers from several shortcomings. These include neglect of systematic empirical studies, descriptive bias, static tendencies, reification, reductionism, elite bias, and monolithic tendencies. In addition to a critique of extant movement framing literature. I offer several remedies and illustrate them with recent work. The articles by Francesca Polletta, John H. Evans, Sharon Erickson Nepstad, and Ira Silver in this special section address several of the concerns raised in this critique and, in so doing, contribute to the integration of structural and cultural approaches to social movements.
On December 3, 2014, after a grand jury decided not to indict the white police officer in the death of Eric Garner, the social networking platform Twitter was flooded with tweets sharing stances on racial profiling and police brutality. To examine how issues concerning race were communicated and exchanged during this time, this study compares differences between tweets using two trending hashtags #CrimingWhileWhite (#cww) and #AliveWhileBlack (#awb) from December 3 through December 11, 2014. To this end, network and content analysis are used on a large dataset of tweets containing the hashtags #awb and #cww. Findings indicate that there are clear differences, both structurally and in linguistic style, between how individuals express themselves based on which hashtag they used. Specifically, we found that #cww users disproportionately shared informational content, which may have led to the hashtag gaining more network volume and attention as a trending topic than #awb. In contrast, #awb tweets tended to be more subjective, expressing a sense of community and strong negative sentiment toward persistent structural racism.
Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed, and further studied users' choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals' choices about what to consume had a stronger effect limiting exposure to cross-cutting content.
Copyright © 2015, American Association for the Advancement of Science.
People turn to social media to express their emotions surrounding major life events. Death of a loved one is one scenario in which people share their feelings in the semi-public space of social networking sites. In this paper, we present the results of a two-part investigation of grief and distress in the context of messages posted to the profiles of deceased MySpace users. We present coding system for identifying emotion distressed content, followed by a detailed analysis of language use that lays a foundation for natural language processing (NLP) tasks, such as automatic detection of bereavement-related distress. Our findings suggest that in addition to words bearing positive or negative sentiment, linguistic style can be an indicator of messages that demonstrate distress in the space of post-mortem social media content. These results contribute to research in computational linguistics by identifying linguistic features that can be used for automatic classification as well as to research on death and bereavement by enumerating attributes of distressed self-expression in a post-mortem context.
Racial and ethnic biases often manifest without awareness. The underlying causes of these attitudes are not fully understood. While outgroup bias is well studied, ingroup bias has received far less attention. We examined ingroup biases among Hispanic women and outgroup biases toward Hispanics among White (Caucasian non-Hispanic) females using the startle eyeblink paradigm, the Implicit Association Test (IAT), and an explicit self-report measure. Hispanic and White male faces were used as exemplars during both the startle task and the IAT. A similar pattern of results were observed for indirect measures: both groups displayed startle and IAT responses indicative of negative attitudes toward Hispanic male faces relative to White male faces, although less so for Hispanic participants. Combined groups correlational analyses revealed a significant positive relationship between startle eyeblink amplitude and subtle subscale bias scores. However, no relationships were found between any measures when groups were examined separately. The comparable pattern of startle and IAT results suggests that in spite of the likelihood that these measures index different aspects of attitudes and tap into different processes, inter and intragroup biases are manifested similarly. The finding of negative ingroup biases among Hispanic females is consistent with system justification theory, which posits that members of devalued groups internalize negative stereotypes about their ingroup. This study extends startle eyeblink research of intergroup racial biases, while also expanding this line of research to intragroup biases. In doing so, these results add to our knowledge of the mechanisms underlying the persistent nature of stereotypes.
Both candidates and voters have increased their use of the Internet for political campaigns. Candidates have adopted many internet tools, including social networking websites, for the purposes of communicating with constituents and voters, collecting donations, fostering community, and organizing events. On the other side, voters have adopted Internet tools such as blogs and social networking sites to relate to candidates, engage in political dialogue, pursue activist causes, and share information. In this paper we examine two years of posts on the Facebook walls of the three major contenders for the U.S. Presidency in 2008: Barack Obama, Hillary Clinton, and John McCain. We analyze participation patterns of usage along dimensions of breadth and frequency, and interpret them in terms of the concept of the "public sphere".
We study the change in polarization of hashtags on Twitter over time and show that certain jumps in polarity are caused by "hijackers" engaged in a particular type of hashtag war.
Online health communities (OHCs) provide support across conditions; for weight loss, OHCs offer support to foster positive behavior change. However, weight loss behaviors can also be subverted on OHCs to promote disordered eating practices. Using comments as proxies for support, we use computational linguistic methods to juxtapose similarities and differences in two Reddit weight loss communities, r/proED and r/loseit. We employ language modeling and find that word use in both communities is largely similar. Then, by building a word embedding model, specifically a deep neural network on comment words, we contrast the context of word use and find differences that imply different behavior change goals in these OHCs. Finally, these content and context norms predict whether a comment comes from r/proED or r/loseit. We show that norms matter in understanding how different OHCs provision support to promote behavior change and discuss the implications for design and moderation of OHCs.
This research examines Twitter discourse related to #BlackLivesMatter and police-related shooting events in 2016 through a mixed-method, interpretative approach. We construct a "shared audience graph", revealing structural and ideological disparities between two groups of participants (one on the political left, the other on the political right). We utilize an integrated networked gatekeeping and framing lens to examine how #BlackLivesMatter frames were produced--and how they were contested--by separate communities of supporters and critics. Among other empirical findings, this work demonstrates hashtags being used in diverse ways--e.g. to mark participation, assert individual identity, promote group identity, and support or challenge a frame. Considered from a networked gatekeeping perspective, we illustrate how hashtags can serve as channeling mechanisms, shaping trajectories of information flow. This analysis also reveals a right-leaning community of BlackLivesMatter critics to have a more well-defined group of crowdsourced elite who largely define their side's counter-frame.
In the face of many cases of sexism within the sciences, scientists have turned to Twitter with hashtags to address the issue. Popular hashtags include #distractinglySexy, #iLookLikeAnEngineer, and #girlsWithToys. In this study, we set out to investigate why people were reading and using these hashtags and what impact it had on them. We administered a survey, which contained multiple choice and free response questions, to 83 people who had read or used these hashtags. We found that the hashtags ended up serving as an ad hoc online community in which participants found a space of social support. Building an improved sense of awareness was also an important reason for and outcome of participation. We examine the role of hashtags as ad hoc online communities on Twitter and the affects that they have for participants.
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
In the face of many cases of sexism within the sciences, scientists have turned to Twitter with hashtags to address the issue. Popular hashtags include #distractinglySexy, #iLookLikeAnEngineer, and #girlsWithToys. In this study, we set out to investigate why people were reading and using these hashtags and what impact it had on them. We administered a survey, which contained multiple choice and free response questions, to 83 people who had read or used these hashtags. We found that the hashtags ended up serving as an ad hoc online community in which participants found a space of social support. Building an improved sense of awareness was also an important reason for and outcome of participation. We examine the role of hashtags as ad hoc online communities on Twitter and the affects that they have for participants.
Conventional wisdom suggests that Donald Trump’s rhetoric – aggressive, insulting, often offensive – would be counterproductive to electoral success. We argue that Trump’s surprising victories in both the primary and general campaigns were partly due to the positive effects of his appeals grounded in the intersection of threat and absolutism. The content of Trump’s rhetoric focused on threats to personal safety (terrorism), personal status (economic decline), and group status (immigration). The style of Trump’s rhetoric was absolutist, emphasizing non-negotiable boundaries and moral outrage at their violation. Previous research has shown perceived threat to motivate political participation and absolutist rhetoric to bolster impressions of positive character traits. Trump employed these two rhetorical psychologies simultaneously, melding threat and absolutism into the absolutist threat as an effective rhetorical strategy. Analysis of Trump’s debate language and Twitter rhetoric, as well as original data from political elites at the Republican National Convention and ordinary voters at rallies in New Hampshire confirm the unconventional efficacy of Trump’s rhetorical approach.
This article examines humor as it intersects with race and gender in digital media. It takes up the idea of laughter to explore how Black expressive culture emerges online, both individually and collectively, in the contemporary moment, arguing that web-based objects such as blogs and podcasts as well as tweets, hashtags, and memes that exist and circulate on social media produce racialized and gendered humor predicated on ridicule. Such ridicule is tied to a genealogy of Black feminist and Black queer enactments of “sass” and “shade” as affective strategies of social scrutiny. By detailing the humor associated with the popular viral personalities Luvvie Ajayi and Crissle West as well as the social networking platform Twitter, this article begins the work of archiving Black women's daily comedic performances on the Internet.
College students are exposed to a vast amount of information on political and social issues through social media. We studied how college students engage with political and social issues on Facebook in a weeklong study with 50 participants using diary entries and interviews. The interviews revealed a duality of benefits for using Facebook for news and they also revealed challenges of self-presentation around news. Benefits included gaining multiple perspectives and enabling social sense making, providing personal relevance and convenience of access, and the opportunity for social engagement. Impression management and disclosure concerns strongly influence some people to refrain from commenting or sharing news. We discuss the challenges that context collapse poses for Facebook news readers. We suggest design ideas and future directions to encourage more balanced participation with news among college students.
In online communities, antisocial behavior such as trolling disrupts constructive discussion. While prior work suggests that trolling behavior is confined to a vocal and antisocial minority, we demonstrate that ordinary people can engage in such behavior as well. We propose two primary trigger mechanisms: the individual's mood, and the surrounding context of a discussion (e.g., exposure to prior trolling behavior). Through an experiment simulating an online discussion, we find that both negative mood and seeing troll posts by others significantly increases the probability of a user trolling, and together double this probability. To support and extend these results, we study how these same mechanisms play out in the wild via a data-driven, longitudinal analysis of a large online news discussion community. This analysis reveals temporal mood effects, and explores long range patterns of repeated exposure to trolling. A predictive model of trolling behavior shows that mood and discussion context together can explain trolling behavior better than an individual's history of trolling. These results combine to suggest that ordinary people can, under the right circumstances, behave like trolls.
On December 3, 2014, after a grand jury decided not to indict the white police officer in the death of Eric Garner, the social networking platform Twitter was flooded with tweets sharing stances on racial profiling and police brutality. To examine how issues concerning race were communicated and exchanged during this time, this study compares differences between tweets using two trending hashtags #CrimingWhileWhite (#cww) and #AliveWhileBlack (#awb) from December 3 through December 11, 2014. To this end, network and content analysis are used on a large dataset of tweets containing the hashtags #awb and #cww. Findings indicate that there are clear differences, both structurally and in linguistic style, between how individuals express themselves based on which hashtag they used. Specifically, we found that #cww users disproportionately shared informational content, which may have led to the hashtag gaining more network volume and attention as a trending topic than #awb. In contrast, #awb tweets tended to be more subjective, expressing a sense of community and strong negative sentiment toward persistent structural racism.
We investigate the role of homophily – a tendency to interact with similar individuals–in the diffusion of political information in social networks. We develop a model predicting disproportionate exposure to like-minded information and that larger groups have more connections and are exposed to more information. To test these hypotheses, we use data on links and communications between politically-engaged Twitter users. We find that users affiliated with majority political groups, relative to the minority group, have more connections, are exposed to more information, and are exposed to information more quickly. Likewise, we find that users are disproportionately exposed to like-minded information and that information reaches like-minded users more quickly.
This research surveys the current state‐of‐the‐art technologies that are instrumental in the adoption and development of fake news detection. “Fake news detection” is defined as the task of categorizing news along a continuum of veracity, with an associated measure of certainty. Veracity is compromised by the occurrence of intentional deceptions. The nature of online news publication has changed, such that traditional fact checking and vetting from potential deception is impossible against the flood arising from content generators, as well as various formats and genres.
The paper provides a typology of several varieties of veracity assessment methods emerging from two major categories – linguistic cue approaches (with machine learning), and network analysis approaches. We see promise in an innovative hybrid approach that combines linguistic cue and machine learning, with network‐based behavioral data. Although designing a fake news detector is not a straightforward problem, we propose operational guidelines for a feasible fake news detecting system.
A number of social media studies have equated people's emotional states with the frequency with which they use affectively positive and negative words in their posts. We explore how such word frequencies relate to a ground truth measure of both positive and negative emotion for 515 Facebook users and 448 Twitter users. We find statistically significant but very weak (ρ in the 0.1 to 0.2 range) correlations between positive and negative emotion-related words from the Linguistic Inquiry Word Count (LIWC) dictionary and a well-validated scale of trait emotionality called the Positive and Negative Affect Schedule (PANAS). We test this for tweets and Facebook status updates, focus on different time slices around the completion of the survey, and consider participants who report expressing emotions frequently on social media. With rare exception, this pattern of low correlation persists, suggesting that for the typical user, dictionary-based sentiment analysis tools may not be sufficient to infer how they truly feel.
Instagram, a popular global mobile photo-sharing platform, involves various user interactions centered on posting images accompanied by hashtags. Participatory hashtagging, one of these diverse tagging practices, has great potential to be a communication channel for various organizations and corporations that would like to interact with users on social media. In this paper, we aim to characterize participatory hashtagging behaviors on Instagram by conducting a case study of its representative hashtagging practice, the Weekend Hashtag Project, or #WHP. By conducting a user study using both quantitative and qualitative methods, we analyzed the way Instagram users respond to participation calls and identified factors that motivate users to take part in the project. Based on these findings, we provide design strategies for any interested parties to interact with users on social media.
We predict that people with different political orientations will exhibit systematically different levels of political homophily, the tendency to associate with others similar to oneself in political ideology. Research on personality differences across the political spectrum finds that both more conservative and more politically extreme individuals tend to exhibit greater orientations towards cognitive stability, clarity, and familiarity. We reason that such a "preference for certainty" may make these individuals more inclined to seek out the company of those who reaffirm, rather than challenge, their views. Since survey studies of political homophily face well-documented methodological challenges, we instead test this proposition on a large sample of politically engaged users of the social-networking platform Twitter, whose ideologies we infer from the politicians and policy nonprofits they follow. As predicted, we find that both more extreme and more conservative individuals tend to be more homophilous than more liberal and more moderate ones.
Sociologists and political scientists have drawn attention to the importance of hashtags as tools for political organizing and protest online. Examining a set of political hashtags on Tumblr, we discover linguistic changes in the ways that the hashtags are used over time. As time passes, the community uses a more diverse vocabulary of hashtags in conjunction with the central political tag, suggesting that the meaning of the tag expands or proliferates -- a phenomenon we dub hashtag drift. We also find evidence that this occurs not just on the level of the community but at the level of individual users who, as time progresses, use the hashtag in conjunction with a more diverse vocabulary of other tags. This study sheds light on the dynamics of political engagement through hashtag activism.
On the afternoon of 9 August 2014, 18-year-old Michael ‘Mike’ Brown was shot and killed by Officer Darren Wilson in the small American city of Ferguson, Missouri. Brown's body lay in the street for four and a half hours, and during that time, his neighbors and friends took to social media to express fear, confusion, and outrage. We locate early tweets about Ferguson and the use of the hashtag #Ferguson at the center of a counterpublic network that provoked and shaped public debates about race, policing, governance, and justice. Extending theory on networked publics, we examine how everyday citizens, followed by activists and journalists, influenced the #Ferguson Twitter network with a focus on emergent counterpublic structure and discursive strategy. We stress the importance of combining quantitative and qualitative methods to identify early initiators of online dissent and story framing. We argue that initiators and their discursive contributions are often missed by methods that collapse longitudinal network data into a single snapshot rather than investigating the dynamic emergence of crowdsourced elites over time.
Revealing what lies behind much contemporary political rhetoric, Morgan Marietta shows that the language of America's most prominent leaders often relies on deep, even sacred, ideals. Comprehensively and in great detail surveying the rhetorical inventions employed in influential social movements and into the highest levels of government, The Politics of Sacred Rhetoric systematically analyzes the use of absolutist claims-and appeals to what a speaker deems to be universal truths-as essential elements of persuasion in the American political landscape. In exploring the sometimes subtle ways in which politicians employ this"sacred rhetoric,"Marietta engagingly demonstrates its impact on citizens' reasoning, public discourse, and the very nature of American democracy.
‘Religion and politics’, as the old saying goes, ‘should never be discussed in mixed company.’And yet fostering discussions that cross lines of political difference has long been a central concern of political theorists. More recently, it has also become a cause célèbre for pundits and civic-minded citizens wanting to improve the health of American democracy. But only recently have scholars begun empirical investigations of where and with what consequences people interact with those whose political views differ from their own. Hearing the Other Side examines this theme in the context of the contemporary United States. It is unique in its effort to link political theory with empirical research. Drawing on her empirical work, Mutz suggests that it is doubtful that an extremely activist political culture can also be a heavily deliberative one.
In this article we investigate the hijacking of the Twitter hashtag #myNYPD following the launch of a public relations campaign by the New York City Police Department in April of 2014. Theorizing networked counterpublics, we examine how Twitter was used as a platform to generate and promote counterpublic narratives about racial profiling and police misconduct. Through a combination of large-scale network analysis and qualitative discourse analysis, we detail counterpublic structure and leadership, discursive strategies deployed by crowdsourced elites, and the reception of counterpublic activism in mainstream media. We conclude with implications for understanding the evolving nature of counterpublics, with particular consideration to the roles of new and old media in (re)shaping public debates around marginalization, profiling, and policing.
We use national survey data to examine the extent to which various sources of political information expose people to dissimilar political views. We hypothesize that the individual's ability and desire to exercise selective exposure is a key factor in determining whether a given source produces exposure to dissimilar views. Although a lack of diverse perspectives is a common complaint against American news media, we find that individuals are exposed to far more dissimilar political views via news media than through interpersonal political discussants. The media advantage is rooted in the relative difficulty of selectively exposing oneself to those sources of information, as well as the lesser desire to do so, given the impersonal nature of mass media.
Framing has become one of the most popular areas of research for scholars in communication and a wide variety of other disciplines, such as psychology, behavioral economics, political science, and sociology. Particularly in the communication discipline, however, ambiguities surrounding how we conceptualize and therefore operationalize framing have begun to overlap with other media effects models to a point that is dysfunctional. This article provides an in-depth examination of framing and positions the theory in the context of recent evolutions in media effects research. We begin by arguing for changes in how communication scholars approach framing as a theoretical construct. We urge scholars to abandon the general term “framing” altogether and instead distinguish between different types of framing. We also propose that, as a field, we refocus attention on the concept's original theoretical foundations and, more important, the potential empirical contributions that the concept can make to our field and our understanding of media effects. Finally, we discuss framing as a bridge between paradigms as we shift from an era of mass communication to one of echo chambers, tailored information and microtargeting in the new media environment. © 2015 Mass Communication & Society Division of the Association for Education in Journalism and Mass Communication
As thousands of demonstrators took to the streets of Ferguson, Missouri, to protest the fatal police shooting of unarmed African American teenager Michael Brown in the summer of 2014, news and commentary on the shooting, the protests, and the militarized response that followed circulated widely through social media networks. Through a theorization of hashtag usage, we discuss how and why social media platforms have become powerful sites for documenting and challenging episodes of police brutality and the misrepresentation of racialized bodies in mainstream media. We show how engaging in “hashtag activism” can forge a shared political temporality, and, additionally, we examine how social media platforms can provide strategic outlets for contesting and reimagining the materiality of racialized bodies. Our analysis combines approaches from linguistic anthropology and social movements research to investigate the semiotics of digital protest and to interrogate both the possibilities and the pitfalls of engaging in “hashtag ethnography.”
The recent concurrence of violent political rhetoric and violence against political targets in the U.S. and abroad has raised public concern about the effects of language on citizens. Building from theoretical foundations in aggression research, I fielded two nationally representative survey experiments and a third local experiment preceding the 2010 midterm elections to investigate support for violence against political authority. Subjects were randomly assigned to view one of two forms of the same political advertisements. Across all three experiments, mild violent metaphors multiply support for political violence among aggressive citizens, especially among young adults. Aggressive personality traits also predict support for political violence in both national studies. This work identifies dynamic roots of violent political orientations and reveals for the first time surprising interactions between this elite discourse and personality traits in citizens.
This study traces the rhythms of news storytelling on Twitter via the #egypt hashtag. Using computational discourse analysis, we examine news values and the form of news exhibited in #egypt from January 25 to February 25, 2011, pre- and post-resignation of Hosni Mubarak. Results point to a hybridity of old and newer news values, with emphasis on the drama of instantaneity, the crowdsourcing of elites, solidarity, and ambience. The resulting stream of news combines news, opinion, and emotion to the point where discerning one from the other is difficult and doing so misses the point. We offer a theory of affective news to explain the distinctive character of content produced by networked publics in times of political crisis.
We consider social media as a promising tool for public health, focusing on the use of Twitter posts to build predictive models about the forthcoming influence of childbirth on the behavior and mood of new mothers. Using Twitter posts, we quantify postpartum changes in 376 mothers along dimensions of social engagement, emotion, social network, and linguistic style. We then construct statistical models from a training set of observations of these measures before and after the reported childbirth, to forecast significant postpartum changes in mothers. The predictive models can classify mothers who will change significantly following childbirth with an accuracy of 71%, using observations about their prenatal behavior, and as accurately as 80-83% when additionally leveraging the initial 2-3 weeks of postnatal data. The study is motivated by the opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness postpartum.