Asymmetric Dynamics of Partisan Warriors in YouTube Comments
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917357938540544 |
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| author | Lee, Keyeun Kim, Sang Jung |
| author_facet | Lee, Keyeun Kim, Sang Jung |
| contents | Cross-cutting commenting on social media is often imagined as a path to deliberation, yet exposure to opposing views frequently fuels hostility. To explain this dynamic, we introduce the concept of partisan warriors--commenters who cross ideological lines primarily to launch uncivil attacks against out-partisans. We analyze a large corpus of YouTube comments (N= 1,854,320) surrounding the 2024 U.S. second presidential debate. After filtering for toxicity and active participation, we use large language models to identify attack targets and operationalize partisan warrior behavior. Our analysis highlights four dynamics. First, cross-cutting commenters do not exhibit greater civility than those who remain within their ideological camps (RQ1). Second, audience reactions diverge by ideology: conservative audiences tended to reward hostile attacks on out-group leaders, whereas liberal audiences offered no comparable incentives and at times penalized such attacks (RQ2). Third, partisan warriors are notably more prevalent in conservative-leaning channels than in liberal ones; commenters restricted to conservative spaces were substantially more likely to engage in partisan warrior behavior compared to their liberal-only counterparts (RQ3). Finally, regarding environmental triggers, robustness checks suggest that this participation is an ecological phenomenon driven largely by channel-level heterogeneity rather than transient responses to individual video titles (RQ4). By shifting attention from the prevalence of incivility to its targets, rewards, and structural drivers, this study advances understanding of how partisan hostility is enacted and sustained in online spaces. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_21776 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Asymmetric Dynamics of Partisan Warriors in YouTube Comments Lee, Keyeun Kim, Sang Jung Social and Information Networks Computers and Society Cross-cutting commenting on social media is often imagined as a path to deliberation, yet exposure to opposing views frequently fuels hostility. To explain this dynamic, we introduce the concept of partisan warriors--commenters who cross ideological lines primarily to launch uncivil attacks against out-partisans. We analyze a large corpus of YouTube comments (N= 1,854,320) surrounding the 2024 U.S. second presidential debate. After filtering for toxicity and active participation, we use large language models to identify attack targets and operationalize partisan warrior behavior. Our analysis highlights four dynamics. First, cross-cutting commenters do not exhibit greater civility than those who remain within their ideological camps (RQ1). Second, audience reactions diverge by ideology: conservative audiences tended to reward hostile attacks on out-group leaders, whereas liberal audiences offered no comparable incentives and at times penalized such attacks (RQ2). Third, partisan warriors are notably more prevalent in conservative-leaning channels than in liberal ones; commenters restricted to conservative spaces were substantially more likely to engage in partisan warrior behavior compared to their liberal-only counterparts (RQ3). Finally, regarding environmental triggers, robustness checks suggest that this participation is an ecological phenomenon driven largely by channel-level heterogeneity rather than transient responses to individual video titles (RQ4). By shifting attention from the prevalence of incivility to its targets, rewards, and structural drivers, this study advances understanding of how partisan hostility is enacted and sustained in online spaces. |
| title | Asymmetric Dynamics of Partisan Warriors in YouTube Comments |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2603.21776 |