# Towards an Automated Framework to Audit Youth Safety on TikTok

arXiv: https://arxiv.org/abs/2509.05838 · HTML: https://mlsanity.com/paper/2509.05838/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2025.

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

- [The Tone of Awareness: Topic, Sentiment, and Toxicity Maps During Mental Health Month on TikTok](https://mlsanity.com/paper/2606.13581/) (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)

This paper investigates the effectiveness of TikTok's enforcement mechanisms for limiting the exposure of harmful content to youth accounts. We collect over 7000 videos, classify them as harmful vs not-harmful, and then simulate interactions using age-specific sockpuppet accounts through both passive and active engagement strategies. We also evaluate the performance of large language (LLMs) and vision-language models (VLMs) in detecting harmful content, identifying key challenges in precision and scalability. Preliminary results show minimal differences in content exposure between adult and youth accounts, raising concerns about the platform's age-based moderation. These findings suggest that the platform needs to strengthen youth safety measures and improve transparency in content moderation.