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        <title>Detecting Child Objectification on Social Media: Challenges in Language Modeling</title>
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    <abstract>Online objectification of children can harm their self-image and influence how others perceive them. Objectifying comments may start with a focus on appearance but also include language that treats children as passive, decorative, or lacking agency. On TikTok, algorithm-driven visibility amplifies this focus on looks. Drawing on objectification theory, we introduce a Child Objectification Language Typology to automatically classify objectifying comments. Our dataset consists of 562,508 comments from 9,090 videos across 482 TikTok accounts. We compare language models of different complexity, including an n-gram-based model, RoBERTa, GPT-4, LlaMA, and Mistral. On our training dataset of 6,000 manually labeled comments, we found that RoBERTa performed best overall in detecting appearance- and objectification-related language. 10.35% of comments contained appearance-related language, while 2.90% included objectifying language. Videos with school-aged girls received more appearance-related comments compared to boys in that age group, while videos with toddlers show a slight increase in objectification-related comments compared to other age groups. Neither gender alone nor engagement metrics showed significant effects.The findings raise concerns about children’s digital exposure, emphasizing the need for stricter policies to protect minors.</abstract>
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%0 Conference Proceedings
%T Detecting Child Objectification on Social Media: Challenges in Language Modeling
%A Schirmer, Miriam
%A Voggenreiter, Angelina
%A Pfeffer, Juergen
%A Horvat, Agnes
%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 schirmer-etal-2025-detecting
%X Online objectification of children can harm their self-image and influence how others perceive them. Objectifying comments may start with a focus on appearance but also include language that treats children as passive, decorative, or lacking agency. On TikTok, algorithm-driven visibility amplifies this focus on looks. Drawing on objectification theory, we introduce a Child Objectification Language Typology to automatically classify objectifying comments. Our dataset consists of 562,508 comments from 9,090 videos across 482 TikTok accounts. We compare language models of different complexity, including an n-gram-based model, RoBERTa, GPT-4, LlaMA, and Mistral. On our training dataset of 6,000 manually labeled comments, we found that RoBERTa performed best overall in detecting appearance- and objectification-related language. 10.35% of comments contained appearance-related language, while 2.90% included objectifying language. Videos with school-aged girls received more appearance-related comments compared to boys in that age group, while videos with toddlers show a slight increase in objectification-related comments compared to other age groups. Neither gender alone nor engagement metrics showed significant effects.The findings raise concerns about children’s digital exposure, emphasizing the need for stricter policies to protect minors.
%U https://aclanthology.org/2025.woah-1.36/
%P 396-412

Markdown (Informal)

[Detecting Child Objectification on Social Media: Challenges in Language Modeling](https://aclanthology.org/2025.woah-1.36/) (Schirmer et al., WOAH 2025)

• Detecting Child Objectification on Social Media: Challenges in Language Modeling (Schirmer et al., WOAH 2025)

ACL

• Miriam Schirmer, Angelina Voggenreiter, Juergen Pfeffer, and Agnes Horvat. 2025. Detecting Child Objectification on Social Media: Challenges in Language Modeling. In Proceedings of the The 9th Workshop on Online Abuse and Harms (WOAH), pages 396–412, Vienna, Austria. Association for Computational Linguistics.