\section{Summary and Discussion}
\label{sec:discussion}


%This study provides a comprehensive examination of language patterns in TikTok comments on videos featuring children, focusing on appearance-related and objectification-related discourse. 
Overall, 10.35\% of comments are related to physical appearance and 2.90\% of comments are  objectifying based on our typology. 
%Our analysis highlights the prevalence of aesthetic descriptions, with words like \textit{"beautiful," "pretty," and "gorgeous"} dominating appearance-related comments. In contrast, objectifying language is marked by terms such as \textit{"my little," "model," and "legs"}, indicating a shift towards evaluating children based on body features rather than their overall identity. 
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 fine-tuned models appear to outperform large-scale generative 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 \citep{sidani2023hypersexualization}. This supports broader findings that girls and women are frequently judged based on their appearance \citep{zurbriggen2007apa}. Objectification-related 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 \citep{gerrard2020content, gongane2022detection}. Platforms must enhance content moderation and improve AI detection of inappropriate language. Raising awareness about sharenting could also help reduce unintended exposure and exploitation \citep{polito2022compliance, stephenson2024sharenting}.




%\paragraph{Language and Objectification in TikTok Comments.}
%Our Child Objectification Language Typology was designed to distinguish between different forms of appearance-based commentary, separating neutral aesthetic descriptions from problematic objectification-related language. 
%The classification results show that our Child Objectification Language Typology effectively captures distinct types of objectifying language on social media. 
%The typology’s subcategories, including possessiveness, diminutive framing, and sexualization, were reflected in the distribution of language observed in objectification-related comments. For example, terms such as \textit{"my little"} highlight possessive and diminutive framing. Additionally, comments referencing physical traits and overall looks, such as \textit{"legs"} or \textit{"model"}, align with the sexualization subcategory, supporting prior research on objectification theory and gendered discourse \citep{fredrickson1997objectification}. 


\section{Limitations}

%To further improve and validate the typology, future work could draw more explicitly from theoretical literature on objectification \citep{calogero2021smile, fredrickson1997objectification} and related constructs such as implicit bias \citep{caliskan2022gender} or harmful speech \citep{fortuna2018survey, matter2024investigating}. Incorporating these frameworks could help refine annotation guidelines and sharpen classification criteria. Studies examining complex social phenomena through NLP—such as toxicity detection \citep{davis2018objectification, kovacs2021challenges}—have shown that grounding computational models in well-defined theoretical typologies improves both reliability and interpretability \citep{breazu2025large, hovy2021importance}. 


\paragraph{Performance of Language Models in Classification Tasks.}
Across all models, detecting objectification remained challenging with low to moderate F1-scores. However, the F1 scores obtained are consistent with similar setups regarding, for example, implicit hate speech \citep{elsherief-etal-2021-latent, li2024hot}, misogyny \citep{park2017private, zeinert2021annotating}, and trauma detection \citep{schirmer2023uncovering, schirmer2024language}. The n-gram-based approach performed competitively to LLMs, indicating that simpler linguistic feature-based methods may  have value in identifying objectification. 
Few-shot prompting led to modest performance improvements, which is consistent with prior research on online harm \citep{agarwal2023haterephrase, nozza2021exposing, pan2024comparing}. However, GPT-4 performed slightly worse in one few-shot setting, and Mistral showed no difference between the two.
Improvement through few-shot learning might thus depend on model architecture and task details \citep{plaza2023respectful}.
Finally, class imbalance likely impacted performance, with objectifying comments underrepresented in the data and models biased toward the majority class \citep{buda2018systematic}. Still, representing objectifying comments in their real-world proportion is important for improving model robustness.

\noindent\textbf{Annotation and Data.}
The subjectivity of annotating objectification-related comments led to only moderate agreement. Given these challenges, it is unreasonable to expect perfect accuracy from language models \citep{li2024hot}. However, their ability to detect nuanced patterns at scale may allow them to recognize implicit objectification more consistently than rule-based approaches \citep{gligoric2024can, matter2024investigating, wang2024human}.
%Future work should explore hybrid approaches, combining human oversight with LLM-based classification, to better navigate the complexities of objectification detection on social media.
The dataset was collected from accounts with high follower counts. Therefore, these videos are likely to exhibit higher overall engagement. The observed prevalence of such comments may not be representative of less visible or lower-engagement content on the platform.




\noindent\textbf{Gender and Metadata Differences.}
Engagement metrics (i.e., the number of downloads and likes) showed no significant association with appearance-related or objectifying comments, suggesting that while engagement may contribute to the visibility of content \citep{kopecky2020phenomenon, schirmer2024more}, it is not a primary driver of objectifying language. With overall low model fits and small effect sizes for all predictors, the results must be interpreted with caution. They likely capture only a limited part of a more complex interplay involving platform norms, audience composition, and broader social context.

%While female videos receive more objectification-related comments overall, the increase in such comments with higher social interaction (total comments) is slightly greater for female videos compared to male videos. However, the relatively small size of this interaction effect suggests that the escalation of objectification in response to higher engagement follows a similar trajectory for both genders, rather than disproportionately intensifying for female-featured videos. For future work, it would be interesting to further specify the interactions of user engagement and gender \citep{sivak2019parents}.



\noindent\textbf{Context and Real-World Implications.}
To make these findings more generalizable, future research should explore cross-platform comparisons \citep{horvat2021birds, matassi2021agenda}. 
%Additionally, longitudinal studies tracking changes in language use over time could provide deeper insights into how objectification-related discourse evolves. 
Given the psychological and social implications of these findings, further work is needed to assess the real-world impact of such comments on children’s self-perception and digital well-being \citep{garmendia2022sharenting, ouvrein2019sharenting}. Additionally, expanding the dataset with richer context, such as including longer comment threads, structured vignettes, or multimodal analysis, could improve model sensitivity to implicit forms of objectification \citep{chistova2022discourse, muti2022unibo, rehman2025context, schirmer2025talking}. 



% \subsection{Conclusion}

% Our research highlights the significant role of appearance-related and objectification-related language in shaping discourse around children on TikTok. These linguistic patterns are not just reflective of individual user interactions but are embedded within broader social and algorithmic dynamics that amplify aesthetic evaluations of minors. The disproportionate focus on female children underscores ongoing gendered biases in digital engagement. 

% This study also emphasizes the challenges and opportunities in leveraging language models for online content moderation. While fine-tuned models outperform general-purpose LLMs, continued advancements in natural language processing are necessary to improve the detection of subtle and implicit objectification. 

% Ultimately, addressing these issues requires a combination of policy interventions, platform-based moderation, and digital literacy efforts to foster a safer online environment for children. By shedding light on these dynamics, our study contributes to the growing discourse on child safety, social media ethics, and responsible AI implementation in content moderation.
