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

Conference paper (2024)

Authors

S. Zannettou Max Planck Institut für Informatik, Organisation & Governance - TPM
Olivia Nemes-Nemeth Max Planck Institute for Software Systems
Oshrat Ayalon University of Haifa, Max Planck Institute for Software Systems
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
Research Group
Organisation & Governance (TPM) (TU Delft)
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Published Date

2024

Language

English

Faculty

Technology, Policy and Management

Department

Multi Actor Systems

Research Group

Organisation & Governance

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.