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宣布推出全新AI系统，用于对Instagram用户可见的评论进行排序。鉴于现有研究表明信息流个性化系统可能加剧观点极化，这一新系统的引入引发了类似关切。本文通过小规模探索性研究，考察该排序系统是否会对不同用户产生系统性差异化的可见评论展示，特别是针对新闻类内容。我们使用四个在性别和政治倾向方面存在差异的傀儡账户，收集来自十个新闻账号和十个非新闻账号帖子的可见评论。为评估地理位置影响，我们通过两个VPN节点重复采集数据。本研究旨在探讨三个问题：1）不同用户看到的可见评论数量差异有多大；2）新闻账号与非新闻账号相比是否呈现更高差异度；3）用户属性（如性别、政治倾向和地理位置）能否系统解释观测到的差异。与研究假设相悖，我们发现新闻帖子的可见评论跨用户差异度反而低于非新闻帖子。相较于用户属性，评论数、粉丝数等账户指标能更好解释差异现象。这些发现初步揭示了Instagram的个性化评论排序机制，并推动开展更大规模、更系统化的审计研究，以探究评论个性化对网络舆论生态的塑造作用。为支持后续研究，我们提供评论采集代码，并可按要求共享数据。