Algorithmic Audit of Personalisation Drift in Polarising Topics on TikTok

Pecher, B., Bindas, A., Jakubcik, J., Tuna, M., Tibensky, M., Liska, S., Sakalik, P., Suty, A., Mosnar, M., Hossner, F., Srba, I.

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.

Cite: Pecher, B., Bindas, A., Jakubcik, J., Tuna, M., Tibensky, M., Liska, S., … & Srba, I. (2026, June). Algorithmic Audit of Personalisation Drift in Polarising Topics on TikTok. In Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization (pp. 1-10).