{"id":23907,"date":"2025-11-03T19:59:49","date_gmt":"2025-11-03T19:59:49","guid":{"rendered":"https:\/\/socialmedialab.ca\/web\/?p=23907"},"modified":"2025-11-06T15:58:18","modified_gmt":"2025-11-06T15:58:18","slug":"new-paper-alert-online-toxic-speech-as-positioning-acts-hate-as-discursive-mechanisms-for-othering-and-belonging","status":"publish","type":"post","link":"https:\/\/socialmedialab.ca\/web\/2025\/11\/03\/new-paper-alert-online-toxic-speech-as-positioning-acts-hate-as-discursive-mechanisms-for-othering-and-belonging\/","title":{"rendered":"[New Paper Alert] Online toxic speech as positioning acts: Hate as discursive mechanisms for othering and belonging"},"content":{"rendered":"\n
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Toxic speech is an endemic threat to civil discourse on social media; it\u2019s marked by incivility, intolerance, and the intent to harm through threats, insults, and patronizing language. Further challenging is the fact that platforms\u2019 content moderation policies often overlook this behaviour, as toxic speech drives user engagement and aids platforms\u2019 bottom line: money. Toxic speech is usually conceptualized as an anti-social action and is associated with several social problems, including misinformation, bullying, and violence. However, this framing alone is reductive. It can also be an act of sociality in communities, marking to the world who belongs <\/em>and who doesn\u2019t<\/em>. This means that we need to refine where we situate toxic speech on the spectrum of anti- to pro-social behaviour to understand both its harms and benefits in online settings.    <\/p>\n\n\n\n

In a recently published study in New Media & Society<\/em>, \u201cOnline toxic speech as positioning acts: Hate as discursive mechanisms for othering and belonging<\/a>,\u201d researchers Esteban Morales (University of Groningen), Jaigris Hodson (Royal Roads University), Victoria O\u2019Meara (University of Leicester), Anatoliy Gruzd (Toronto Metropolitan University), and Philip Mai (Toronto Metropolitan University) took a further look at how toxic speech is used by social media users to construct and maintain in- and out-group boundaries in online communities.<\/p>\n\n\n\n

Methodology<\/strong><\/h2>\n\n\n\n

The research team used Communalytic, a computational social science research tool, <\/a>to collect 98,729 publicly available posts from the popular Colombian Telegram group Chismes Frescos Medellin (Fresh Gossip Medellin)<\/em>. Telegram is particularly useful for examining toxic speech for two reasons. First, the platform is continually gaining popularity, serving as a key space for socializing and sharing news. Second, Telegram is notorious for its stance on content moderation, rejecting censorship in favour of free speech. This conscious decision has made it a hotbed for violent content and communities.<\/p>\n\n\n\n

Toxic posts were identified using the machine-learning classifier Detoxify, built into Communalytic\u2019s Civility Analyzer module. The Civility Analyzer assigned a score from 0 (least toxic) to 1 (most toxic); only those posts assigned a score greater than 0.7 were retained (3,221 posts). A manual review of posts identified 66 cases of misclassification, leading to a final sample of 3,155 posts. Posts were then topically categorized, with those posts in the four most prevalent topics (1,258 posts, nearly 40% of the final sample) serving as the object for the study\u2019s analysis. These topics included: security <\/em>(652 posts, 20%); politics <\/em>(252, 7.8%); migration <\/em>(188 posts, 5.8%); and gender <\/em>(166 posts, 5.2%). <\/p>\n\n\n\n

To analyze the sample, our group used a thematic analysis to qualitatively tease out who <\/em>was being positioned in each topic\u2019s toxic posts \u2014 the<\/em> self<\/em>, the<\/em> other<\/em>, and the<\/em> group \u2014 <\/em>and how <\/em>these positionalities are negotiated across the observed conversations.<\/p>\n\n\n\n

Results<\/strong><\/h2>\n\n\n\n

To answer our research question (“How do Telegram group members use toxic speech to position themselves and others in relation to narratives emerging from the group?<\/em>“), we identified four key themes from an analysis of 1,258 posts:<\/p>\n\n\n\n

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