Towards an Automated Framework to Audit Youth Safety on TikTok
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914026274947072 |
|---|---|
| author | Xue, Linda Corso, Francesco Fontana, Nicolo' Liu, Geng Ceri, Stefano Pierri, Francesco |
| author_facet | Xue, Linda Corso, Francesco Fontana, Nicolo' Liu, Geng Ceri, Stefano Pierri, Francesco |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards an Automated Framework to Audit Youth Safety on TikTok Xue, Linda Corso, Francesco Fontana, Nicolo' Liu, Geng Ceri, Stefano Pierri, Francesco Computers and Society K.4.1 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. |
| title | Towards an Automated Framework to Audit Youth Safety on TikTok |
| topic | Computers and Society K.4.1 |
| url | https://arxiv.org/abs/2509.05838 |