# An Empirical Investigation of Personalization Factors on TikTok

arXiv: https://arxiv.org/abs/2201.12271 · HTML: https://mlsanity.com/paper/2201.12271/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2022.

## Built on by (most cited first, 9 of 9)

- [Human-AI Coevolution](https://mlsanity.com/paper/2306.13723/) (2023)
- [MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform](https://mlsanity.com/paper/2506.00308/) (2025)
- [Dynamics of Algorithmic Content Amplification on TikTok](https://mlsanity.com/paper/2503.20231/) (2025)
- [Is computational creativity flourishing on the dead internet?](https://mlsanity.com/paper/2407.17590/) (2024)
- [Algorithmic Behaviors Across Regions: A Geolocation Audit of YouTube Search for COVID-19 Misinformation Between the United States and South Africa](https://mlsanity.com/paper/2409.10168/) (2024)
- [DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph](https://mlsanity.com/paper/2306.13837/) (2023)
- [When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai](https://mlsanity.com/paper/2605.23598/) (2026)
- [Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale](https://mlsanity.com/paper/2606.30801/) (2026)
- [Auditing Exposure to Harmful Content on TikTok using Multimodal Language Models: A Cross-National, Age-Stratified Study](https://mlsanity.com/paper/2608.17583/) (2026)

## Abstract (arXiv)

TikTok currently is the fastest growing social media platform with over 1 billion active monthly users of which the majority is from generation Z. Arguably, its most important success driver is its recommendation system. Despite the importance of TikTok's algorithm to the platform's success and content distribution, little work has been done on the empirical analysis of the algorithm. Our work lays the foundation to fill this research gap. Using a sock-puppet audit methodology with a custom algorithm developed by us, we tested and analysed the effect of the language and location used to access TikTok, follow- and like-feature, as well as how the recommended content changes as a user watches certain posts longer than others. We provide evidence that all the tested factors influence the content recommended to TikTok users. Further, we identified that the follow-feature has the strongest influence, followed by the like-feature and video view rate. We also discuss the implications of our findings in the context of the formation of filter bubbles on TikTok and the proliferation of problematic content.