Social media platforms have become an integral part of everyday life, serving as a primary source of news and information for many users. These platforms increasingly rely on personalised recommendation systems that shape what users see and engage with. While these systems are optimised for engagement, concerns have emerged that they may also drive users toward more polarised perspectives, particularly in contested domains such as politics, climate change, vaccines, and conspiracy theories. In this paper, we present an algorithmic audit of personalisation drift on TikTok in these polarising topics. Using controlled accounts designed to simulate users with interests aligned with or opposed to different polarising topics, we systematically measure the extent to which TikTok steers content exposure toward specific topics and polarities over time. Specifically, we investigated: 1) a preference-aligned drift (showing a strong personalisation towards user interests), 2) a polarisation-topic drift (showing a strong neutralising effect for misinformation-themed topics, and a high preference and reinforcement of interest of US politic topic); and 3) a polarisation-stance drift (showing a preference of oppose stance towards US politics topic and a general reinforcement of users' stance by recommending items aligned with their stance towards polarising topics). Overall, our findings provide evidence that recommendation trajectories differ markedly across topics, with some pathways amplifying polarised viewpoints more strongly than others and offer insights for platform governance, transparency and user awareness.

翻译：社交媒体平台已成为日常生活的重要组成部分，是许多用户获取新闻和信息的主要来源。这些平台日益依赖个性化推荐系统，这些系统塑造了用户所看到和参与的内容。尽管这些系统以用户参与度为导向进行优化，但人们担心它们也可能使用户走向更极化的观点，尤其是在政治、气候变化、疫苗和阴谋论等有争议的领域。在本文中，我们对TikTok上这些极化话题中的个性化漂移进行了算法审计。通过使用设计的控制账户模拟对不同极化话题持有赞同或反对立场的用户，我们系统地衡量了TikTok随时间将内容曝光引导至特定话题及立场倾向的程度。具体而言，我们研究了：1）偏好对齐漂移（展示出对用户兴趣的强烈个性化倾向）；2）极化话题漂移（对虚假信息类话题展示出强烈的中和效应，而对美国政治话题则表现出高度偏好和兴趣强化）；以及3）极化立场漂移（对美国政治话题展示出反对立场偏好，并通过推荐与用户立场一致的项目，普遍强化用户对极化话题的立场）。总体而言，我们的研究结果表明，不同话题的推荐轨迹存在显著差异，某些路径较其他路径更强烈地放大了极化观点，这为平台治理、透明度和用户意识提供了启示。