@inproceedings{xue-etal-2025-towards,
title = "Towards an Automated Framework to Audit Youth Safety on {T}ik{T}ok",
author = "Xue, Linda and
Corso, Francesco and
Fontana, Nicolo and
Liu, Geng and
Ceri, Stefano and
Pierri, Francesco",
editor = "Blodgett, Su Lin and
Curry, Amanda Cercas and
Dev, Sunipa and
Li, Siyan and
Madaio, Michael and
Wang, Jack and
Wu, Sherry Tongshuang and
Xiao, Ziang and
Yang, Diyi",
booktitle = "Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.hcinlp-1.9/",
pages = "113--119",
ISBN = "979-8-89176-353-1",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Towards an Automated Framework to Audit Youth Safety on TikTok
%A Xue, Linda
%A Corso, Francesco
%A Fontana, Nicolo
%A Liu, Geng
%A Ceri, Stefano
%A Pierri, Francesco
%Y Blodgett, Su Lin
%Y Curry, Amanda Cercas
%Y Dev, Sunipa
%Y Li, Siyan
%Y Madaio, Michael
%Y Wang, Jack
%Y Wu, Sherry Tongshuang
%Y Xiao, Ziang
%Y Yang, Diyi
%S Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-353-1
%F xue-etal-2025-towards
%X 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.
%U https://aclanthology.org/2025.hcinlp-1.9/
%P 113-119
Markdown (Informal)
[Towards an Automated Framework to Audit Youth Safety on TikTok](https://aclanthology.org/2025.hcinlp-1.9/) (Xue et al., HCINLP 2025)
ACL
- Linda Xue, Francesco Corso, Nicolo Fontana, Geng Liu, Stefano Ceri, and Francesco Pierri. 2025. Towards an Automated Framework to Audit Youth Safety on TikTok. In Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP), pages 113–119, Suzhou, China. Association for Computational Linguistics.