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        <title>Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation</title>
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    <abstract>The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as external APIs, suffer from limitations in customisation, accuracy across diverse sensitive categories, and privacy concerns. Additionally, existing datasets and open-source models focus predominantly on toxic language, leaving gaps in detecting other sensitive categories such as substance abuse or self-harm. In this paper, we put forward a unified dataset tailored for social media content moderation across six sensitive categories: conflictual language, profanity,sexually explicit material, drug-related content, self-harm, and spam. By collecting and annotating data with consistent retrieval strategies and guidelines, we address the shortcomings of previous focalised research. Our analysis demonstrates that fine-tuning large language models (LLMs) on this novel dataset yields significant improvements in detection performance compared to open off-the-shelf models such as LLaMA, and even proprietary OpenAI models, which underperform by 10-15% overall. This limitation is even more pronounced on popular moderation APIs, which cannot be easily tailored to specific sensitive content categories, among others.</abstract>
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%0 Conference Proceedings
%T Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation
%A Antypas, Dimosthenis
%A Sen, Indira
%A Perez Almendros, Carla
%A Camacho-Collados, Jose
%A Barbieri, Francesco
%Y Calabrese, Agostina
%Y de Kock, Christine
%Y Nozza, Debora
%Y Plaza-del-Arco, Flor Miriam
%Y Talat, Zeerak
%Y Vargas, Francielle
%S Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-105-6
%F antypas-etal-2025-sensitive
%X The detection of sensitive content in large datasets is crucial for ensuring that shared and analysed data is free from harmful material. However, current moderation tools, such as external APIs, suffer from limitations in customisation, accuracy across diverse sensitive categories, and privacy concerns. Additionally, existing datasets and open-source models focus predominantly on toxic language, leaving gaps in detecting other sensitive categories such as substance abuse or self-harm. In this paper, we put forward a unified dataset tailored for social media content moderation across six sensitive categories: conflictual language, profanity,sexually explicit material, drug-related content, self-harm, and spam. By collecting and annotating data with consistent retrieval strategies and guidelines, we address the shortcomings of previous focalised research. Our analysis demonstrates that fine-tuning large language models (LLMs) on this novel dataset yields significant improvements in detection performance compared to open off-the-shelf models such as LLaMA, and even proprietary OpenAI models, which underperform by 10-15% overall. This limitation is even more pronounced on popular moderation APIs, which cannot be easily tailored to specific sensitive content categories, among others.
%U https://aclanthology.org/2025.woah-1.2/
%P 17-31

Markdown (Informal)

[Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation](https://aclanthology.org/2025.woah-1.2/) (Antypas et al., WOAH 2025)

• Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation (Antypas et al., WOAH 2025)

ACL

• Dimosthenis Antypas, Indira Sen, Carla Perez Almendros, Jose Camacho-Collados, and Francesco Barbieri. 2025. Sensitive Content Classification in Social Media: A Holistic Resource and Evaluation. In Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH), pages 17–31, Vienna, Austria. Association for Computational Linguistics.