Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917618470879232 |
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| author | Zannettou, Savvas Nemeth, Olivia-Nemes Ayalon, Oshrat Goetzen, Angelica Gummadi, Krishna P. Redmiles, Elissa M. Roesner, Franziska |
| author_facet | Zannettou, Savvas Nemeth, Olivia-Nemes Ayalon, Oshrat Goetzen, Angelica Gummadi, Krishna P. Redmiles, Elissa M. Roesner, Franziska |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_04945 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations Zannettou, Savvas Nemeth, Olivia-Nemes Ayalon, Oshrat Goetzen, Angelica Gummadi, Krishna P. Redmiles, Elissa M. Roesner, Franziska Social and Information Networks Computers and Society 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. |
| title | Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations |
| topic | Social and Information Networks Computers and Society |
| url | https://arxiv.org/abs/2301.04945 |