Paper
You See It, They Don't: An Exploratory Study of User-to-User Variation in Instagram Comments
Authors
Brahmani Nutakki, Manon Lilott Kempermann, Ingmar Weber
Abstract
In March 2025, Meta announced a new AI system to rank the order of the comments shown to Instagram users. With existing research showing how feed personalization systems can lead to increased polarization, the introduction of this new system raises similar questions. This paper presents a small-scale exploratory study examining whether the ranking system produces systematic differences in visible comments shown to different users, particularly for news-related content. Using four sock-puppet accounts varying in gender and political leaning, we collect visible comments on posts from ten news and ten non-news accounts. This collection is repeated twice from two VPN locations to assess location effects. We ask 1) how many visible comments vary across different users, 2) is this variation higher for news accounts than non-news accounts, and 3) can user-attributes like gender, political leaning, and location systematically explain the observed variation. Contrary to our expectations, we find that visible comments on news posts are less likely to vary across users than those on non-news posts. Variation is better explained by account metrics like comment and follower counts than by user attributes. These findings provide an initial glimpse into personalized comment ranking on Instagram and motivate larger, more systematic audits of how comment personalization may shape online discourse. To support further research, we provide the code to collect comments and the data upon request.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.21953v1</id>\n <title>You See It, They Don't: An Exploratory Study of User-to-User Variation in Instagram Comments</title>\n <updated>2026-03-23T13:10:16Z</updated>\n <link href='https://arxiv.org/abs/2603.21953v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.21953v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>In March 2025, Meta announced a new AI system to rank the order of the comments shown to Instagram users. With existing research showing how feed personalization systems can lead to increased polarization, the introduction of this new system raises similar questions. This paper presents a small-scale exploratory study examining whether the ranking system produces systematic differences in visible comments shown to different users, particularly for news-related content. Using four sock-puppet accounts varying in gender and political leaning, we collect visible comments on posts from ten news and ten non-news accounts. This collection is repeated twice from two VPN locations to assess location effects. We ask 1) how many visible comments vary across different users, 2) is this variation higher for news accounts than non-news accounts, and 3) can user-attributes like gender, political leaning, and location systematically explain the observed variation. Contrary to our expectations, we find that visible comments on news posts are less likely to vary across users than those on non-news posts. Variation is better explained by account metrics like comment and follower counts than by user attributes. These findings provide an initial glimpse into personalized comment ranking on Instagram and motivate larger, more systematic audits of how comment personalization may shape online discourse. To support further research, we provide the code to collect comments and the data upon request.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n <published>2026-03-23T13:10:16Z</published>\n <arxiv:comment>This work has been accepted at the International AAAI Conference on Web and Social Media Understanding the World Through the Web (ICWSM) 2026 as a poster. Once available, please cite the peer-reviewed version</arxiv:comment>\n <arxiv:primary_category term='cs.CY'/>\n <author>\n <name>Brahmani Nutakki</name>\n </author>\n <author>\n <name>Manon Lilott Kempermann</name>\n </author>\n <author>\n <name>Ingmar Weber</name>\n </author>\n </entry>"
}