\section{Context and Measurement of Child Objectification}

%Children frequently appear in TikTok videos, often as the central focus of content that ranges from everyday family moments to structured performances in dance, or viral challenges. Many of these videos are shared by parents or influencers who feature children as part of their content. 
%In some cases, entire accounts are dedicated to showcasing a child’s activities, with carefully curated posts designed to attract engagement. 
%This visibility is amplified by TikTok’s algorithm, which prioritizes visually engaging and widely shared content, often pushing videos featuring children to broader audiences. 

%The way children are portrayed on TikTok often blurs the line between visibility and objectification, as their presence can attract inappropriate attention and harmful interactions. 
Although inappropriate content involving minors exists across many platforms, TikTok has faced particular criticism for facilitating the sexual exploitation of children and adolescents \citep{polito2022compliance}. Young users are often drawn to imitate trending content, which can include sensual or provocative dances or appearances in swimwear or underwear \citep{suarez2023sexualising}. In addition, studies found that minors frequently receive sexually explicit comments and requests \citep{silva2019, soriano2023tiktok}.
%Many comments center on children's physical appearance, offering compliments on their looks and, in some cases, escalating to inappropriate interactions such as invitations to meet up, highlighting the risks minors face online \citep{silva2019}. Furthermore, a Forbes investigation uncovered that child sexual abuse material has been shared within private TikTok accounts, exposing deeper vulnerabilities in the platform’s moderation systems \citep{levine2022}.
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 \citep{frederick2022demographic, fardouly2016social}. 
%On TikTok, where virality is largely driven by algorithmic recommendation systems, high-engagement content is more likely to be circulated widely, increasing exposure to objectifying language \citep{harriger2023tiktok}.  
Additionally, girls and young women receive more appearance-based comments than boys on social media \citep{doring2019male, kim2021others}. 

%However, objectification of children remains underexplored, and it is unclear whether similar gendered patterns hold in this context or if objectification manifests differently based on age and platform dynamics.  

%Furthermore, the act of downloading videos, particularly those featuring minors, has raised concerns in the context of digital safety, as certain types of content may be disproportionately saved due to implicit or explicit objectifying motives \citep{livingstone2015one, staksrud2009children}. The relationship between such metadata and objectifying language in comments remains largely unexplored, highlighting the need for systematic analysis. Investigating how demographic factors and engagement metrics influence objectifying language will provide deeper insights into the mechanisms driving such discourse and inform strategies for mitigating potential harms.  

Most insights into the online exploitation of children have come from investigative journalism by major news outlets \citep{nyt2024, silva2019, levine2022, barry2021}, while scientific studies on the topic remain limited. Although broader analyses of children on TikTok are beginning to emerge \citep{stephenson2024sharenting}, academic research has so far focused mainly on qualitative reports and individual case studies \citep{khan2022new, soriano2023tiktok}. 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 \citep{steel2025just}. Large-scale quantitative research focused specifically on children is needed for a more systematic investigation in this area.


\subsection{\emph{Sharenting} on Social Media}
“Sharenting”, a term used to describe parents sharing information about their children on social media, has become increasingly common in today’s digital society \citep{cataldo2022cradle, verswijvel2019sharenting}. Sharenting includes a wide range of activities, from posting photos and videos to sharing personal stories and milestones, often introducing children to the online world from an early age. While sharenting can help families stay connected and celebrate meaningful moments, it also raises important concerns about privacy, consent, and the long-term impact of creating a digital footprint for children \citep{stephenson2024sharenting, walrave2022limits}.
The full extent of sharenting remains unclear and differs across countries and platforms. In a survey of 493 United-States-based parents who regularly use social media, nearly 90\% reported sharing content about their children online \citep{amon2022sharenting}. This practice can infringe on children's right to privacy, especially since many parents do not seek their child’s consent before posting \citep{kopecky2020phenomenon, ni2022privacy, van2024child}. The shared content often includes sensitive information: for instance, in a sample of Facebook posts from 168 parents, 90.5\% mentioned their child’s first name, 83.9\% included birthdates, and 32.7\% shared personal documents or videos \citep{brosch2016child}. 

%Similarly, in a survey of 2,900 Spanish children aged 9 to 17, 20\% reported that their parents shared information about them online, with older children and girls being more frequently affected \citep{garmendia2022sharenting}. 
%Exploring the reasons behind parents' decisions to post content featuring their children online, \citet{latipah2020elaborating} interviewed 10 parents about their activity on social media platforms like Facebook and Instagram. They identified four primary reasons for sharing: seeking affirmation and social support, demonstrating caregiving capabilities, engaging in social participation, and documenting family experiences (cf., \citet{amon2022sharenting} for similar results). Further, \citet{campana2020dadtribe} found that fathers on Instagram share photos with their children to both connect with others and showcase their family lives. Specifically, these fathers also seek to challenge and redefine stereotypes of fatherhood through their social media presence. 

%In a survey with 1,460 Czech and Spanish parents, of whom around 80\% published pictures of their child, only 20\% obtained their child's consent \citep{kopecky2020phenomenon}. Some parents even deliberately ignore the will of their child, as reported in interviews with 12-14-year-olds \citep{ouvrein2019sharenting}. In the study by \citet{garmendia2022sharenting}, around 4\% of children reported negative outcomes from sharenting, such as hurtful comments, and 12\% of children requested their parents to remove shared content about them. 


%In addition, the child-related content shared by parents often violates the child's dignity. \citet{stormer2023caregiver} identified 184 videos of 35 TikTok accounts containing psychological maltreatment towards children through caregivers, such as yelling at, ignoring, and pranking them. They found that these videos received higher engagement in the form of likes, views, and comments than those without maltreatment of children. Even more, in the investigation of \citet{brosch2016child}, about 45\% of the parents posted photos that could be considered inappropriate, such as images of their child in the nude or semi-nude, typically taken during baths or beach visits, involving children under 3 years old. Similarly, in the study of \citet{kopecky2020phenomenon}, 20\% of the parents admitted having posted photos in which their children were partially exposed, and 3.5\% of the Czech sample (n=1,093) had shared photographs of their naked child at a neonatal or infant stage online. These findings are in line with \citet{stephenson2024sharenting}'s conclusion that parents frequently share intimate posts aiming for viral content.
%The content shared by parents often violates the child's dignity. \citet{stormer2023caregiver} identified TikTok videos containing psychological maltreatment towards children through caregivers, such as yelling at and pranking them. These videos received higher engagement in the form of likes, views, and comments than those without maltreatment of children. In an investigation by \citet{brosch2016child}, about 45\% of the parents posted photos that could be considered inappropriate, such as images of babies and toddlers in the nude or semi-nude, typically taken during baths or beach visits. Similarly, \citet{kopecky2020phenomenon} found that 20\% of the parents admitted having posted photos in which their children were partially exposed, and 3.5\% had shared photographs of their naked child at a neonatal or infant stage online. 


%\subsection{Inappropriate Content of Children on TikTok}



\subsection{Measuring Objectifying Language}

%Detecting objectifying language on social media poses significant challenges due to its implicit nature and the complexity of defining and operationalizing objectification in textual data. These expressions can be ambiguous, context-dependent, and shaped by cultural norms, making their automated detection particularly difficult.

\subsubsection{Objectification Theory}

Theories of objectification have been widely explored in psychology, feminist studies, and media research, describing how individuals are reduced to their physical attributes rather than being recognized as full persons. One central framework in this area is \textit{Objectification Theory} \citep{fredrickson1997objectification}. This theory conceptualizes objectification as the process by which individuals are perceived and treated primarily as bodies to be evaluated based on appearance. A key component of this framework is self-objectification, which occurs when individuals internalize an external observer’s perspective, leading them to engage in body surveillance and appearance-based self-evaluation. This phenomenon has been linked to psychological consequences such as increased body shame, anxiety, and reduced cognitive performance, particularly in social media contexts where visual presentation is central \citep{moradi2008objectification}.  
In a related line of research, \citet{glick1997two} introduced \textit{Ambivalent Sexism Theory}. This framework distinguishes between hostile sexism, characterized by overtly negative and demeaning attitudes, and benevolent sexism, which manifests as seemingly positive but ultimately restrictive perceptions that reinforce traditional gender roles. 
%Both forms of sexism contribute to objectification by shaping how individuals, particularly women and girls, are valued based on appearance rather than agency or capability.
%Benevolent sexism is particularly relevant in the context of objectifying language, as it often appears in the form of compliments that nonetheless reduce individuals to their physical appearance. 
Similarly, \textit{The Fragmented Body Theory} \citep{bernard2012integrating, bernard2018objectifying} highlights how media representations frequently depict individuals as isolated body parts rather than whole persons, reinforcing objectifying narratives and shaping the way people perceive and describe others.

%While these theories primarily focus on visual representations of objectification, linguistic research has sought to identify how objectification manifests in language. Objectifying language often fixates on physical attributes rather than intrinsic qualities, reinforcing attractiveness as a primary source of value \citep{fasoli2015social, swim2004understanding}. Such language can take the form of direct comments on physical appearance, sexually suggestive expressions that implicitly frame individuals in a desirable manner, or references that fragment individuals into isolated body parts, echoing the patterns described by fragmented body theory. In some cases, objectification also involves statements that strip individuals of agency, presenting them as passive subjects to be admired or consumed rather than as autonomous individuals \citep{waller-gorman-2020-detecting}. Therefore, while the frameworks mentioned above do not specifically focus on language but rather on broader characteristics of objectification, these traits are reflected in language and can thus be meaningfully applied to a language-based analysis.

\subsubsection{Natural Language Processing for Objectification Detection}
NLP models have been widely used to detect harmful language online, including hate speech, cyberbullying, and toxicity \citep{schmidt2017survey, fortuna2018survey}. However, while hate speech is often characterized by hostility, threats, or dehumanization based on identity \citep{waseem2017understanding, vidgen2020directions}, objectification can occur without overt negativity. Instead--similar to misogynistic language \citep{nozza2019unintended, samghabadi2020aggression}--it can take the form of seemingly positive or neutral language that nonetheless reduces individuals to their appearance or sexualizes them \citep{glick2018ambivalent}. %This makes detection especially challenging, as conventional hate speech models, which rely on explicit markers of aggression, may fail to capture these nuances.

Measuring objectifying language requires a more refined approach that accounts for implicit linguistic cues and context. Prior studies have explored dictionary-based methods and supervised machine learning models to identify objectification \citep{farrell2019exploring, sik2023topic}. While lexicon-based approaches provide a starting point, they struggle with context sensitivity and often lack the ability to distinguish between benign and problematic uses of appearance-related language. Supervised models, trained on manually labeled data, can improve accuracy but are constrained by the quality and representativeness of their training datasets. Advances in the development of LLMs have improved performance in capturing implicit language features by learning contextual patterns \citep{abdurahman2023perils, ding2024can}. Still, language models of all sizes face challenges in distinguishing admiration from objectification, particularly when there is no obvious negativity \citep{elsherief-etal-2021-latent, li2024hot}. 

%For instance, while a single comment describing a child as \textit{“beautiful”} may seem harmless, a persistent pattern of comments fixating on physical traits shifts the focus away from the child's individuality. This becomes even more challenging when distinguishing between expressions like \textit{``beautiful job"}, where we do not know the reference and therefore cannot classify it as objectifying, and \textit{``beautiful little girl"} used to describe a child. Similarly, a comment like \textit{“she’s so beautiful, just like a little model”} may seem like a compliment, but it subtly applies adult beauty standards to a child, reinforcing external valuation. In contrast, \textit{“she has a beautiful singing voice”} shifts the focus away from appearance, demonstrating the challenge of identifying objectification in context. Language models help address this ambiguity by analyzing context and identifying when seemingly neutral language contributes to recurring patterns of appearance-based objectification.

%Despite growing awareness of objectifying language, no dedicated classification models currently exist for detecting objectification in social media contexts. While NLP models have been developed for related issues like hate speech and toxicity, objectification remains understudied, particularly in large-scale, real-world datasets. Existing approaches struggle with the implicit nature of objectifying language, which can be context-dependent and difficult to distinguish from benign comments. 




\subsubsection{Child Objectification Language Typology for Social Media}
Applying traditional objectification theory to social media comments directed at children is complex, as these theories were primarily developed to analyze the objectification of adult women. Regarding children, objectification rarely involves obvious sexualization; instead, it tends to appear in repeated focus on physical appearance, exaggerated admiration, and comparisons that reinforce external valuation. Concepts like infantilization, central to adult objectification, are less applicable here since childlike traits are inherent. %Moreover, the nature of social media—characterized by slang, emojis, and non-standard spelling—adds further challenges to identifying these more implicit forms of objectifying language.
To address these gaps, we propose a \textbf{Child Objectification Language Typology} (Table \ref{tab:child_objectification}). 
This typology encompasses both explicit and subtle forms of objectification in social media discourse by distinguishing general appearance-related remarks and specifically objectifying language. 
%It addresses patterns of diminishing comments, possessiveness, and implicit comparisons in online discussions about children, while providing a scalable approach for computational analysis. To also capture the extent of comments that might not be objectifying per se, but heavily focus on appearance and can thus amplify objectification, we add the category of general appearance-based language to our framework to serve as a baseline for related language context. 
%To ensure the relevance of these categories to our data, we conducted a deductive analysis of a subset of 1,000 comments, assessing whether the predefined categories were applicable or if additional classifications were necessary. Grounded in objectification theory \citep{fredrickson1997objectification, glick2018ambivalent, moradi2008objectification}, this process allowed us to refine our framework and identify four primary ways in which children are objectified in social media interactions.
It categorizes different forms of objectifying language on social media, incorporating concepts from objectification theory adaptable to children. Sexualizing or age-inappropriate language applies adult beauty norms to children, implying maturity beyond their actual age or sexualizing physical traits. Comparative and competitive appearance commentary ranks children’s attractiveness, reinforcing social hierarchies and treating them as objects of comparison. Diminutive framing exaggerates cuteness, portraying children as fragile, doll-like, or dependent rather than recognizing them as developing individuals. Possessive comments include language that implies ownership, entitlement, or undue familiarity. Appearance-based language includes comments that describe a child’s physical features, clothing, or attractiveness. While these comments are not necessarily objectifying, they shift the focus to physical features and are thus included as a baseline. 


\begin{table*}[ht]\small
    \centering
    \renewcommand{\arraystretch}{1.3} % Increase row height for readability
    \setlength{\tabcolsep}{10pt} % Adjust column spacing
    \begin{tabular}{p{3cm}p{5.5cm}p{4.5cm}}
        \toprule
        \textbf{Category} & \textbf{Definition} & \textbf{Examples} \\ 
        \toprule
        
                \textbf{Objectification-Based Language} & & \\
        
        Sexualizing or Age-Inappropriate Comments \vspace{0.2cm} & Implies maturity beyond the child’s age or applies adult attractiveness standards. &  
        \textit{``She’s gonna be a heartbreaker."} \newline
        \textit{``She doesn’t look 12 at all!"} \\  
        
        Comparative Comments & Ranks children's attractiveness, reinforces beauty standards, or compares them to adults. \vspace{0.2cm}&  
        \textit{``She’s the prettiest one in this group."} \newline
        \textit{``Such a strong little man."} \\  
        
        Diminutive Framing \vspace{0.2cm} & Uses baby-like descriptors that emphasize smallness or cuteness. &  
        \textit{``Aww, such a tiny baby doll!"} \newline
        \textit{``She’s just a little angel!"} \\  
        
        Possessiveness & Implies ownership, entitlement, or personal attachment to the child. &  
        \textit{``She’s mine!"} \newline
        \textit{``Our little angel."} \\  

        \midrule

        \textbf{General Appearance-Based Language} & Describes physical features, clothing, or attractiveness. These comments may not be objectifying per se, but can amplify objectification through an emphasis on physical attributes. &  
        \textit{``I like her hair!"} \newline
        \textit{``Where did you buy his shoes?"} \\  


        \bottomrule
    \end{tabular}
    \caption{Categories of child objectification in social media comments.}
    \label{tab:child_objectification}
\end{table*}



%Since the first category focuses on appearance-related vocabulary, we aimed to capture all references to physical appearance—including those that might not be explicitly harmful. This is important, as appearance-based language is a significant part of TikTok discourse, and it can be difficult to determine when such emphasis becomes excessive. Appearance-related comments describe observable traits, such as facial features, clothing, or general attractiveness, without falling under one of the objectification categories defined in our framework (e.g., \textit{beautiful, pretty, tall, dress, eyelashes}). In contrast, objectification-related comments reflect diminutive descriptions, possession, or sexualization, framing the subject as an object rather than an individual (e.g., \textit{my baby, doll, kiss}). This distinction was guided by prior research on objectification theory and gendered language use \citep{fredrickson1997objectification, moradi2008objectification}. 
%These categories help analyze and classify the ways in which social media conversations shape perceptions of children, ensuring that both explicit and subtle forms of objectification are captured.







%\subsubsection{Demographic Factors and Engagement Metrics in Objectifying Language}  





\section{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:
\vspace{0.1cm}

\noindent {\textbf{RQ1:} What linguistic context characterizes how children are discussed in TikTok comments, and how do these patterns relate to objectification?  
\vspace{0.1cm}

\noindent \textbf{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?
\vspace{0.1cm}

\noindent \textbf{RQ3:} How are demographic factors (e.g., gender, age) and metadata (e.g., likes, downloads) related to the prevalence of objectifying language?

