# Conspiracy theories and where to find them on TikTok

arXiv: https://arxiv.org/abs/2407.12545 · HTML: https://mlsanity.com/paper/2407.12545/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2024.

## Built on by (most cited first, 7 of 7)

- [Evaluating open-source Large Language Models for automated fact-checking](https://mlsanity.com/paper/2503.05565/) (2025)
- [Evaluating AI capabilities in detecting conspiracy theories on YouTube](https://mlsanity.com/paper/2505.23570/) (2025)
- [ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety](https://mlsanity.com/paper/2508.20468/) (2025)
- [Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection](https://mlsanity.com/paper/2603.21368/) (2026)
- [Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models](https://mlsanity.com/paper/2511.03699/) (2025)
- [Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization](https://mlsanity.com/paper/2601.17658/) (2026)
- [Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts](https://mlsanity.com/paper/2607.04962/) (2026)

## Abstract (arXiv)

TikTok has skyrocketed in popularity over recent years, especially among younger audiences. However, there are public concerns about the potential of this platform to promote and amplify harmful content. This study presents the first systematic analysis of conspiracy theories on TikTok. By leveraging the official TikTok Research API we collect a longitudinal dataset of 1.5M videos shared in the U.S. over three years. We estimate a lower bound on the prevalence of conspiratorial videos (up to 1000 new videos per month) and evaluate the effects of TikTok's Creativity Program for monetization, observing an overall increase in video duration regardless of content. Lastly, we evaluate the capabilities of state-of-the-art open-weight Large Language Models to identify conspiracy theories from audio transcriptions of videos. While these models achieve high precision in detecting harmful content (up to 96%), their overall performance remains comparable to fine-tuned traditional models such as RoBERTa. Our findings suggest that Large Language Models can serve as an effective tool for supporting content moderation strategies aimed at reducing the spread of harmful content on TikTok.