\section{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 \citep{nyt2024}. 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, TikTok is estimated to have around 900 million users \citep{statista2024}, 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 \citep{glick2018ambivalent}. 

\begin{figure}[b]
  \centering
  \includegraphics[width=0.7\linewidth]{Figures/Children_Examples.pdf}
  \caption{Anonymized examples of typical child video content on TikTok.}
  \label{fig:preview}
\end{figure}

Discussions about children on social media often focus on access, i.e., whether they should be allowed to participate and consume content \citep{martinez2024kidfluencers}. 
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 \citep{amon2022sharenting}. While some of these videos portray children in everyday contexts, others—intentionally or unintentionally—place a strong emphasis on their physical appearance (Figure \ref{fig:preview}). %Prior research suggests that visibility, particularly when focused on appearance, encourages audience interactions that perpetuate objectification on social media \citep{davis2018objectification}. 
%On TikTok, where engagement drives visibility, their appearances can attract disproportionate attention, making them subjects of scrutiny, admiration, and objectification. 

%This study explores how objectification manifests in TikTok comments and whether a computational framework can help identify these patterns.
%Many accounts showcase children in various contexts, including family content, dance performances, and lifestyle videos. The frequent portrayal of children on TikTok raises concerns about how they are perceived and engaged with by audiences, particularly in terms of appearance-based feedback and objectifying language.


Existing studies on online harm, such as hate speech detection and cyberbullying, have developed robust models for identifying harmful language \citep{basile2019semeval, fortuna2018survey, zampieri2019semeval}. However, objectification presents a distinct challenge: it often manifests in subtle, seemingly positive, or ambiguous ways that evade traditional detection methods. Objectifying comments may not contain overt insults or hate speech but instead fixate on a child's physical traits, compare them to adults, or sexualize their appearance under the guise of admiration \citep{bernard2018objectifying, glick2018ambivalent}. %This type of engagement can contribute to harmful patterns of external validation, reinforcing the idea that a child's value is primarily linked to their looks rather than their personality, skills, or achievements. 
Prior work on complex social phenomena in NLP shows that theory-driven approaches improve both reliability and interpretability \citep{davis2018objectification, kovacs2021challenges, breazu2025large, hovy2021importance}, highlighting the need for a theory-grounded typology that captures how objectification is expressed.

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:
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\noindent (1) We introduce the \textbf{Child Objectification Language Typology}. We implement and test this typology on a dataset of 562,508 comments from 9,090 unique videos across 482 TikTok accounts. \footnote{All code is publicly available at \url{https://github.com/MiriamSchirmer/child-objectification}.}
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\noindent (2) We evaluate language models of different complexity, identifying their strengths and limitations in capturing implicit objectification in comments.
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\noindent (3) We find that 10.35\% of comments contain appearance-related language, and 2.90\% include objectifying language. Videos featuring school-aged girls receive more appearance-related comments than those with boys.


%Findings highlight platform dynamics shaping interactions and the need for stronger safeguards. The insights inform discussions on children's digital safety, urging platform and policy interventions, such as stricter content moderation and educating parents on online risks.