Cross-partisan discussions on YouTube: Conservatives talk to liberals but liberals don’t talk to conservatives
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We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.
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On platforms like Twitter, Reddit, or YouTube, polarization dynamics are not primarily ideological disagreements about issues, but dismissive interactions with out-groups.
“Scholars such as Barbera ´(2020) made a strong case that especially on platforms like Twitter, Reddit, or YouTube (An et al., 2019;Wu & Resnick, 2021), polarization dynamics are not primarily ideological disagreements about issues, but dismissive interactions with out-groups.”
From When debates break apart: discursive polarization as a multi-dimensional divergence emerging in and through communication (2023) · cites this paper as Wu & Resnick, 2021
Section 7, “Toxicity as a Measure of Quality” (page not provided), reports that YouTube replies to people of opposing ideologies were much more frequently toxic, supporting the point about dismissive out-group interactions on YouTube. The paper does not establish that such interactions are the primary driver of polarization rather than disagreements about issues. (LLM, conf=0.87)
Experimental — accuracy not yet measuredCross-cutting interactions with opposing groups exist, particularly in political discourses and between politically-interested users.
“Cross-cutting interactions with opposing groups exist, particularly in political discourses and between politically-interested users (An et al., 2019;Wu & Resnick, 2021).”
From When debates break apart: discursive polarization as a multi-dimensional divergence emerging in and through communication (2023) · cites this paper as Wu & Resnick, 2021
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- Authors
- Wu, S., & Resnick, P.
- Affiliations
- Australian National University (AU)
- University of Michigan (US)
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-05-22
- Volume
- 15
- Pages
- 808-819
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W3154639321
- Cited by
- 34
- References
- 62
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 7 · 2023: 5 · 2022: 9 · 2021: 1
- FWCI
- 8.01
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the International AAAI Conference on Web and Social Media (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (1717688)
- Asian Office of Aerospace Research and Development
- Topics
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- YouTube comments, political polarization, echo chambers, partisan media, toxicity, reply behavior, Perspective API, 2020 elections
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