Just Another Hour on TikTok: ID sampling to obtain a complete slice of TikTok

Authors

DOI:

https://doi.org/10.51685/jqd.2026.002

Keywords:

TikTok, random sampling

Abstract

TikTok is now a massive platform, and has a deep impact on global events. Despite preliminary studies, issues remain in determining fundamental characteristics of the platform. We develop a method to extract a representative sample of >99% of posts from a given time range on TikTok, and use it to collect all posts from a full hour on the platform, alongside all posts from a single minute from each hour of a day. Through this, we obtain post metadata, video media, and comments from a close-to-complete slice of TikTok, and report the critical statistics of the platform. Notably, we estimate a total of 269 million posts produced on the day we looked at, that 18% of videos on the platform feature children, and that at least 0.5% of posts contain artificial intelligence-generated content.

Author Biographies

  • Benjamin Steel, McGill University

    PhD Student at the School of Computer Science

  • Dr. Miriam Schirmer, Northwestern University

    Postdoctoral Researcher at LINK Lab

  • Prof. Derek Ruths, McGill University

    Professor at the School of Computer Science

  • Prof. Juergen Pfeffer, Technical University of Munich

    Full Professor (Lehrstuhl) of Computational Social Science at the School of Social Sciences and Technology

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Published

2026-01-12

Issue

Vol. 6 (2026)

Section

Articles

License

Copyright (c) 2026 Benjamin Steel, Dr. Miriam Schirmer, Prof. Derek Ruths, Prof. Juergen Pfeffer

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Steel, B., Schirmer, M., Ruths, D., & Pfeffer, J. (2026). Just Another Hour on TikTok: ID sampling to obtain a complete slice of TikTok. Journal of Quantitative Description: Digital Media, 6. https://doi.org/10.51685/jqd.2026.002