https://doi.org/10.1140/epjds/s13688-026-00629-2
Research
Dynamics of algorithmic content amplification on TikTok
1
Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
2
Department of Biology, University of Pennsylvania, Philadelphia, USA
3
Department of Computational Social Science, Faculty of Management, Wrocław University of Science and Technology, Wrocław, Poland
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
b
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
18
April
2025
Accepted:
5
February
2026
Published online:
11
February
2026
Abstract
Content exposure online is increasingly shaped by socio-technical systems, where human and algorithmic factors interact. TikTok exemplifies strong algorithm-driven curation, tailoring the stream of content towards users’ interests based on their behavior on the platform. Despite growing concerns about TikTok’s societal impact, the dynamics of content amplification at the individual level remain largely unquantified. How quickly, and to what extent, does TikTok amplify content aligned with users’ interests? To address these questions, we conduct a sock-puppet audit, deploying bots with different interests to engage with TikTok’s “For You” feed. Our findings reveal that content aligned with the bots’ interests undergoes strong amplification, with rapid reinforcement typically occurring within the first 200 videos watched. While amplification is consistently observed across all interests, its intensity and dynamics vary, as revealed by time series analyses and Markov models. Although TikTok’s algorithm preserves some content diversity, we find a strong negative correlation between amplification and exploration: as the amplification of interest-aligned content increases, the diversity of encountered hashtags declines sharply. Our findings contribute to discussions on socio-algorithmic feedback loops in the digital age and the trade-offs between personalization and content diversity.
Key words: Social media / TikTok / Algorithmic amplification / Exploration / Sock-puppet audit
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-026-00629-2.
Handling Editor: Shoko Wakamiya
© The Author(s) 2026
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
