Detecting Child Objectification on Social Media: Challenges in Language Modeling (2025)

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.

## introduction recent investigations have raised serious concerns about children's presence on social media. a new york times report uncovered a troubling trend on instagram: a "marketplace of girl influencers," often managed by parents, that draws the attention of individuals with exploitative intentions @xcite . these findings underscore the broader risks associated with children's online presence, including on platforms like tiktok, where short-form videos encourage engagement from vast audiences. as of 2024, tik-tok is estimated to have around 900 million users @xcite , making it a major platform for self-expression, creative content, and social engagement. however, this visibility also exposes children to harmful language, including targeted harassment, inappropriate comments, and objectification. objectification, in this context, refers to language that reduces a child to their physical attributes rather than recognizing them as individuals with agency. this includes excessive focus on appearance, comparisons to adult beauty standards, and possessiveness @xcite . discussions about children on social media often focus on access, i.e., whether they should be allowed to participate and consume content @xcite . a bigger challenge, however, is when children themselves become the content. most often, this content is shared by parents. 90% of parents in the united states who regularly use social media have shared content about their children online @xcite . while some of these videos portray children in everyday contexts, others-intentionally or unintentionally-place a strong emphasis on their physical appearance (figure 1 ). existing studies on online harm, such as hate speech detection and cyberbullying, have developed robust models for identifying harmful lan-figure 1 : anonymized examples of typical child video content on tiktok. this work is centered around the research questions of how objectifying language manifests in tiktok comments on children and what challenges state-of-the-art natural language processing (nlp) models face in detecting it: ## context and measurement of child objectification although inappropriate content involving minors exists across many platforms, tiktok has faced particular criticism for facilitating the sexual exploitation of children and adolescents @xcite . young users are often drawn to imitate trending content, which can include sensual or provocative dances or appearances in swimwear or underwear @xcite . in addition, studies found that minors frequently receive sexually explicit comments and requests @xcite . engagement metrics, such as likes, play a critical role in amplifying certain types of online discourse, including objectification. content emphasizing physical appearance tends to receive higher engagement @xcite . additionally, girls and young women receive more appearance-based comments than boys on social media @xcite . most insights into the online exploitation of children have come from investigative journalism by major news outlets @xcite @xcite , while scientific studies on the topic remain limited. although broader analyses of children on tiktok are beginning to emerge @xcite , academic research has so far focused mainly on qualitative reports and individual case studies @xcite . one exception is a recent study suggesting that up to one fifth of videos on the platform may feature children, though it does not analyze the nature of this content in depth @xcite . large-scale quantitative research focused specifically on children is needed for a more systematic investigation in this area. ## scope and research questions this study addresses how objectification manifests in social media discourse, evaluating the effectiveness of different language models in its detection. to systematically analyze this child objectification, we build on these research questions: rq1: what linguistic context characterizes how children are discussed in tiktok comments, and how do these patterns relate to objectification? rq2: how can we implement a typology to classify and analyze objectifying language in tiktok comments, and how well do different language models perform in this task? rq3: how are demographic factors (e.g., gender, age) and metadata (e.g., likes, downloads) related to the prevalence of objectifying language? ## results answering rq1 on language characteristics associated with comments on tiktok videos featuring children, we found that it is primarily shaped by expressions of affection and cuteness, familial references, aesthetic discussions, and engagement with tiktok trends. overall, this reflects language that focuses on emotional reactions, social aspects, and platform-driven interactions. figure 2 shows the semantic similarity among the 25 most frequent topics, illustrating how different themes cluster together based on shared linguistic patterns in their bert-based embeddings. these embeddings were projected into two dimensions using umap for visualization (see appendix, section a.2 for details). topics related to affection and cuteness, such as those containing words like "cute" and "baby," are closely grouped, reflecting their frequent co-occurrence in sentimental expressions. familyrelated topics, including mentions of "sis," "sister," "momma," and "dad," form distinct clusters, highlighting the strong presence of familial framing in comments. aesthetic discussions are also prominent, with words like "hair," and "shoes" indicating attention to fashion. the influence of tiktok culture is evident in words such as "tiktok(s)". ## summary and discussion overall, 10.35% of comments are related to physical appearance and 2.90% of comments are objectifying based on our typology. these comments are embedded in language that centers around beauty, lifestyle, and expressions of cuteness and admiration. roberta consistently achieved the highest f1-scores across both tasks. in contrast, generative llms struggled to match its performance. for objectification classification, traditional finetuned models appear to outperform large-scale gen-erative approaches, possibly because the task relies less on broad world knowledge and more on recognizing subtle, context-dependent language patterns. school-aged girls received significantly more appearance-related comments than boys of the same age, reflecting gendered patterns observed in earlier studies on social media discourse @xcite . this supports broader findings that girls and women are frequently judged based on their appearance @xcite . objectificationrelated comments appeared slightly more often in videos featuring toddlers than other age groups. the patterns in tiktok comments raise serious concerns about children's online safety. language that objectifies children might reinforce harmful norms and subject children's appearances to scrutiny, impacting digital well-being and privacy. exposure to such comments can increase vulnerability among young users, highlighting the need for stronger protections @xcite . platforms must enhance content moderation and improve ai detection of inappropriate language. raising awareness about sharenting could also help reduce unintended exposure and exploitation @xcite .