You See It, They Don't: An Exploratory Study of User-to-User Variation in Instagram Comments
Brahmani Nutakki, Manon Lilott Kempermann, Ingmar Weber

TL;DR
This exploratory study investigates how Instagram's comment ranking system varies across users, examining the influence of user attributes and content type on comment visibility differences.
Contribution
It provides initial insights into user-to-user variation in comment visibility and highlights the need for larger audits of comment personalization effects.
Findings
Comments on news posts vary less across users than non-news posts.
Variation is better explained by account metrics than user attributes.
The study offers code and data to support further research.
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…
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