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.

翻译：2025年3月，Meta宣布推出一套新的人工智能系统，用于对Instagram用户显示的评论进行排序。鉴于现有研究表明信息流个性化系统可能导致极化加剧，这套新系统的引入引发了类似的问题。本文通过一项小规模探索性研究，考察该排序系统是否会导致不同用户看到的可见评论存在系统性差异，尤其是针对与新闻相关的内容。我们利用四个在性别和政治倾向上各异的傀儡账户，收集了来自十个新闻账户和十个非新闻账户的帖子上的可见评论。为评估地理位置的影响，这一收集过程从两个VPN地点分别重复进行。我们探究以下问题：1）不同用户看到的可见评论数量在不同用户之间存在多大差异？2）新闻账户的该差异是否高于非新闻账户？3）用户属性（如性别、政治倾向和地理位置）能否系统性地解释观察到的差异？与预期相反，我们发现，新闻帖子的可见评论在不同用户之间的变化可能性低于非新闻帖子。相较于用户属性，评论数和关注者数等账户指标能更好地解释这些差异。这些发现初步揭示了Instagram上的个性化评论排序，并推动开展更大规模、更系统化的审计，以探究评论个性化如何影响网络话语。为支持进一步研究，我们提供用于收集评论的代码，并应请求公开数据。