Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
Savvas Zannettou (TU Delft - Organisation & Governance, Max Planck Institut für Informatik)
Olivia Nemes-Nemeth (Max Planck Institute for Software Systems)
Oshrat Ayalon (Max Planck Institute for Software Systems, University of Haifa)
Angelica Goetzen (Max Planck Institute for Software Systems)
Krishna P. Gummadi (Max Planck Institute for Software Systems)
Elissa M. Redmiles (Georgetown University)
Franziska Roesner (University of Washington)
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Abstract
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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