# Dynamics of Algorithmic Content Amplification on TikTok

arXiv: https://arxiv.org/abs/2503.20231 · HTML: https://mlsanity.com/paper/2503.20231/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Machine Culture](https://mlsanity.com/paper/2311.11388/) (2023)
- [Auditing Radicalization Pathways on YouTube](https://mlsanity.com/paper/1908.08313/) (2019)
- [Algorithmic Amplification of Politics on Twitter](https://mlsanity.com/paper/2110.11010/) (2021)
- [Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media](https://mlsanity.com/paper/2305.16941/) (2023)
- [Modeling echo chambers and polarization dynamics in social networks](https://mlsanity.com/paper/1906.12325/) (2019)
- [Emergence of polarized ideological opinions in multidimensional topic spaces](https://mlsanity.com/paper/2007.00601/) (2020)
- [An Empirical Investigation of Personalization Factors on TikTok](https://mlsanity.com/paper/2201.12271/) (2022)
- [Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations](https://mlsanity.com/paper/2301.04945/) (2023)
- [TikTok and the Art of Personalization: Investigating Exploration and Exploitation on Social Media Feeds](https://mlsanity.com/paper/2403.12410/) (2024)
- [Optimal Engagement-Diversity Tradeoffs in Social Media](https://mlsanity.com/paper/2303.03549/) (2023)
- [Studying Behavioral Addiction by Combining Surveys and Digital Traces: A Case Study of TikTok](https://mlsanity.com/paper/2501.15539/) (2025)
- [Auditing the Biases Enacted by YouTube for Political Topics in Germany](https://mlsanity.com/paper/2107.09922/) (2021)
- [YouTube Recommendations Reinforce Negative Emotions: Auditing Algorithmic Bias with Emotionally-Agentic Sock Puppets](https://mlsanity.com/paper/2501.15048/) (2025)
- [Interacting discovery processes on complex networks](https://mlsanity.com/paper/2012.06369/) (2020)
- [Modeling Rabbit-Holes on YouTube](https://mlsanity.com/paper/2307.09986/) (2023)

## Built on by (most cited first, 2 of 2)

- [When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai](https://mlsanity.com/paper/2605.23598/) (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)

Intelligent algorithms increasingly shape the content we encounter and engage with online. TikTok's For You feed exemplifies extreme algorithm-driven curation, tailoring the stream of video content almost exclusively based on users' explicit and implicit interactions with the platform. Despite growing attention, the dynamics of content amplification on TikTok remain largely unquantified. How quickly, and to what extent, does TikTok's algorithm amplify content aligned with users' interests? To address these questions, we conduct a sock-puppet audit, deploying bots with different interests to engage with TikTok's "For You" feed. Our findings reveal that content aligned with the bots' interests undergoes strong amplification, with rapid reinforcement typically occurring within the first 200 videos watched. While amplification is consistently observed across all interests, its intensity varies by interest, indicating the emergence of topic-specific biases. Time series analyses and Markov models uncover distinct phases of recommendation dynamics, including persistent content reinforcement and a gradual decline in content diversity over time. Although TikTok's algorithm preserves some content diversity, we find a strong negative correlation between amplification and exploration: as the amplification of interest-aligned content increases, engagement with unseen hashtags declines. These findings contribute to discussions on socio-algorithmic feedback loops in the digital age and the trade-offs between personalization and content diversity.