Censoring political opposition online: Who does it and why

Ashokkumar, Ashwini; Talaifar, Sanaz; Fraser, William T.; Landabur, Rodrigo; Buhrmester, Michael D.; Gómez, Ángel; Paredes, Borja; Swann, William B.

doi:10.1016/j.jesp.2020.104031

Cited by 30 publications

(30 citation statements)

References 69 publications

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“…To our knowledge, this is the first study to show that group identity strength can fluctuate over short periods of time and as a function of ordinary events like joining and leaving of groups. Second, this work goes beyond previous research on the online behaviors of strongly identified individuals ( 33 , 34 ) by opening the possibility of studying identity processes in emerging identity-based online groups. This approach can be especially useful in studying extreme groups that have been linked with contemporary social problems (e.g.…”

Section: Discussionmentioning

confidence: 97%

Tracking group identity through natural language within groups

Ashokkumar¹,

Pennebaker

2022

PNAS Nexus

Self Cite

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To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a–1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students’ likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates—Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members’ joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

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Section: Discussionmentioning

confidence: 97%

Tracking group identity through natural language within groups

Ashokkumar¹,

Pennebaker

2022

PNAS Nexus

Self Cite

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“…Research suggests that identity fusion predicts extreme pro-group outcomes in a variety of contexts, including national sentiments (Gómez et al, 2016;, political extremism (Ashokkumar et al, 2020;Besta et al, 2015;Kunst et al, 2019), sport contexts (Bortolini et al, 2018) and religiously motivated conflicts (Fredman et al, 2017), among others. However, a systematic meta-analysis of the available research is missing to date.…”

Section: Identity Fusion and Extreme Pro-group Outcomes: Remaining Qu...mentioning

confidence: 99%

How Identity Fusion Predicts Extreme Pro-Group Orientations: A Meta-Analysis

Varmann¹,

Kruse²,

Bierwiaczonek³

et al. 2022

Preprint

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Identity fusion reflects a visceral feeling of oneness with a group that predicts extreme pro-group orientations. While the theory has been tested extensively, several questions have not been conclusively answered. Here, we present the first meta-analysis of the associations between identity fusion and pro-group orientations (k = 57, N = 36,880, 106 effects). Identity fusion had a strong positive association with extreme pro-group orientations, which was significantly stronger than between social identification and these outcomes. The observed relationships were relatively robust across various countries and, interestingly, the effects were strongest for fusion with an outgroup. The verbal scale assessing identity fusion was the most predictive, and extreme forms of collective action were most strongly correlated with fusion, followed by sacrifice- and die orientations and outgroup hostility. In sum, the present meta-analysis provides evidence for identity fusion being robustly associated with extreme pro-group outcomes across contexts and settings.

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“…Today, the perception of moral convergence is a frequently experienced phenomenon among social network users. Indeed, moral values—especially that of moral purity (e.g., religiosity, sacredness)—have been identified as a source of homophily and an important factor in the formation of bonds and communities online (Dehghani et al, 2016), often resulting in moral echo chambers with highly exaggerated perceptions of homogeneity among the members (Ashokkumar et al, 2020; Price et al, 2006).…”

Section: Morally Motivated Violencementioning

confidence: 99%

Morally Homogeneous Networks and Radicalism

Atari

Davani

Kogon

et al. 2021

Social Psychological and Personality Science

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Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab—a right-wing extremist network—the degree of moral convergence within a cluster predicts the number of hate-speech messages members post. In Study 2, we replicate this observation in another extremist network, Incels. In Studies 3 to 5 ( N = 1,431), we demonstrate that experimentally leading people to believe that others in their hypothetical or real group share their moral views increases their radical intentions as well as willingness to fight and die for the group. Our findings highlight the role of moral convergence in radicalization, emphasizing the need for diversity of moral worldviews within social networks.

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Censoring political opposition online: Who does it and why

Cited by 30 publications

References 69 publications

Tracking group identity through natural language within groups

Tracking group identity through natural language within groups

How Identity Fusion Predicts Extreme Pro-Group Orientations: A Meta-Analysis

Morally Homogeneous Networks and Radicalism

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