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Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations

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arxiv 2301.04945 v2 pith:5Q7UJWCD submitted 2023-01-12 cs.SI cs.CY

Savvas Zannettou , Olivia-Nemes Nemeth , Oshrat Ayalon , Angelica Goetzen , Krishna P. Gummadi , Elissa M. Redmiles , Franziska Roesner This is my paper · ORCID
classification cs.SIcs.CY
keywords tiktokdatausersuservideosengagementpeopleshort-format
verification ladder T0 review T1 audit T2 compute T3 formal
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Short-format videos have exploded on platforms like TikTok, Instagram, and YouTube. Despite this, the research community lacks large-scale empirical studies into how people engage with short-format videos and the role of recommendation systems that offer endless streams of such content. In this work, we analyze user engagement on TikTok using data we collect via a data donation system that allows TikTok users to donate their data. We recruited 347 TikTok users and collected 9.2M TikTok video recommendations they received. By analyzing user engagement, we find that the average daily usage time increases over the users' lifetime while the user attention remains stable at around 45%. We also find that users like more videos uploaded by people they follow than those recommended by people they do not follow. Our study offers valuable insights into how users engage with short-format videos on TikTok and lessons learned from designing a data donation system.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Auditing Meta and TikTok Research API Data Access under Article 40(12) of the Digital Services Act

    cs.CY 2026-01 conditional novelty 7.0 of 10

    TikTok and Meta research APIs expose only about 75% and 50% of user-visible posts, respectively, and strip most contextual metadata, making independent auditing of systemic risks structurally biased.

  2. Towards an Automated Framework to Audit Youth Safety on TikTok

    cs.CY 2025-09 conditional novelty 5.0 of 10

    An audit of TikTok in Italy finds that accounts set to age 13 and 18+ receive similar levels of harmful content, especially when actively searching.

  3. AutoLike: Auditing Social Media Recommendations through User Interactions

    cs.LG 2025-02 conditional novelty 4.0 of 10

    AutoLike frames recommendation auditing as reinforcement learning and shows that automated like/skip interactions can push TikTok's feed to serve about 2x more content on a chosen topic and sentiment than a skip-only control.

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