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

arXiv: https://arxiv.org/abs/2301.04945 · HTML: https://mlsanity.com/paper/2301.04945/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2023.

## Built on by (most cited first, 8 of 8)

- [Dynamics of Algorithmic Content Amplification on TikTok](https://mlsanity.com/paper/2503.20231/) (2025)
- [Causally-Informed Reinforcement Learning for Adaptive Emotion-Aware Social Media Recommendation](https://mlsanity.com/paper/2511.14768/) (2025)
- [A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement](https://mlsanity.com/paper/2604.19995/) (2026)
- [Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto](https://mlsanity.com/paper/2506.11112/) (2025)
- [Generative Propaganda](https://mlsanity.com/paper/2509.19147/) (2025)
- [TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation](https://mlsanity.com/paper/2605.13433/) (2026)
- [When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts](https://mlsanity.com/paper/2606.00046/) (2026)
- [Compass: Continuously Aligning Social Media Feeds via In-Situ Reflections](https://mlsanity.com/paper/2608.04274/) (2026)

## Abstract (arXiv)

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.