\section{Results}


%\subsection{Language Context of TikTok-Comments on Child Videos}

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 \ref{fig:topic-embeddings} 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 \nameref{sec:app}, Section \ref{app:tm} 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. Family-related 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)''.
%Mentions of ``song," ``music," and ``tune" reflect the role of audio in this type of TikTok content.
%Expressions like ``wow" indicate emotional engagement. Additionally, references to growth and change, seen in words like ``growing" and ``grew," show discussions of children's development over time. 

\begin{figure}[ht]
  \centering
  \includegraphics[width=0.98\linewidth]{Figures/prominent_topic_visualization_log.pdf}
  \caption{Top 25 topic clusters based on BERT embeddings of comments, reduced to two dimensions using UMAP. Each point represents a comment, positioned according to its semantic similarity to others. Point size reflects the topic frequency.}
  \label{fig:topic-embeddings}
\end{figure}

\begin{table*}[ht!]\small
    \setlength{\tabcolsep}{4pt}
    \centering
    \begin{tabular}{rcccccccc}
    \toprule
    Classifier & \multicolumn{4}{c}{General Appearance} & \multicolumn{4}{c}{Objectification} \\
    \cmidrule(r){1-1} \cmidrule(lr){2-5} \cmidrule(l){6-9}
    LM & F1 (bin.) & Prec. & Recall & AU-ROC & F1 (bin.) & Prec. & Recall & AU-ROC \\
    \midrule
    NGramNeuralNetwork       & 0.46 & 0.59 & 0.38 & 0.72 & 0.42 & 0.46 & 0.39 & 0.67 \\
    RoBERTa (finetuned)      & \textbf{0.74} & \textbf{0.62} & 0.91 & \textbf{0.98} & \textbf{0.51} & \textbf{0.63} & 0.44 & \textbf{0.97} \\
    OpenAI GPT-4 (zero-shot) & 0.48 & 0.50 & 0.47 & 0.72 & 0.18 & 0.21 & 0.15 & 0.57 \\
    OpenAI GPT-4 (few-shot)  & 0.51 & 0.59 & 0.44 & 0.71 & 0.08 & 0.05 & 0.27 & 0.54 \\
    LLaMA 3-7B (zero-shot)   & 0.42 & 0.35 & 0.54 & 0.73 & 0.18 & 0.11 & 0.40 & 0.65 \\
    LLaMA 3-7B (few-shot)    & 0.45 & 0.40 & 0.52 & 0.73 & 0.25 & 0.17 & 0.47 & 0.69 \\
    Mistral-7B (zero-shot)   & 0.16 & 0.09 & 0.95 & 0.62 & 0.08 & 0.04 & 0.54 & 0.56 \\
    Mistral-7B (few-shot)    & 0.16 & 0.09 & \textbf{0.97} & 0.62 & 0.08 & 0.04 & \textbf{0.69} & 0.58 \\
    \bottomrule
    \end{tabular}
    \caption{Classification performance of the language models used in this work. We report Binary F1-Scores, Precision, Recall, and AU-ROC. Bold values indicate the best-performing model for each metric.}
    \label{tab:classification-performance}
\end{table*}




\subsection{Objectification Detection}

Table \ref{tab:classification-performance} presents the performance of our classification models in detecting general appearance-related and objectification-related language in TikTok comments (RQ2). 
For detecting general appearance-related language, RoBERTa achieved the highest F1-score (0.74), along with the top precision (0.62) and AU-ROC (0.98) values. Mistral (few-shot) reached the highest recall (0.97), but with very low precision (0.09), indicating a high rate of false positives and thus limited reliability. Among the generative LLMs, GPT-4 (few-shot) performed best with an F1-score of 0.51, slightly ahead of the open-source models. The NGram Neural Network performed comparably to GPT-4 (zero-shot), achieving an F1-score of 0.46, suggesting that lexical patterns still play a notable role in identifying appearance-related language.
For objectification-related language, overall model performance was lower. Again, RoBERTa achieved the best F1-score (0.51) and led across most metrics. Among generative LLMs, LLaMA 3-7B (few-shot) showed relatively stronger results, reaching an F1-score of 0.25, surpassing GPT-4 and Mistral in this task.
Overall, RoBERTa was the top-performing model across both classification tasks. Among open-source models, LLaMA 3-7B (few-shot) offered the strongest alternative for both appearance and objectification detection. The NGram Neural Network performed competitively with GPT-4, nearly matching its performance on general appearance and outperforming it in objectification detection. 
With few-shot settings, most models showed modest gains over their zero-shot counterparts.
Only in one case, objectification classification with GPT-4, the few-shot variant led to worse results. This might be explained by the provided examples limiting the models’ flexibility or introducing biases. 
For Mistral, F1 scores were identical for both settings.


%The results suggest that objectification detection remains more challenging than general appearance classification, as seen in lower F1-scores across all models. The tendency of Mistral-7B and LLaMA 3-7B to overpredict in zero-shot settings reinforces the importance of balancing precision and recall in detecting harmful language. These findings highlight that large-scale fine-tuned models or hybrid approaches may be necessary to achieve robust objectification detection, particularly in sensitive contexts like child safety online. 




\paragraph{Error Analysis.}  

A closer look at misclassified comments revealed that context seems to be essential in determining whether a phrase is objectifying in certain cases. One recurring example that all language models misclassified is the phrase ``my little one”, which appeared in sentences with varying interpretations. When used by a parent to refer to their own child (e.g., \textit{``My little one also loves these sweets!”}), the phrase does not refer to the child in the video and is thus not objectifying. However, when the unrelated child in the video is referenced (e.g., \textit{``She’s so precious, she's my little one''}), it can imply a sense of symbolic possession, fitting within the possessiveness category of objectification. 
Another example that posed challenges were comments that specifically contained sexualizing language but were not directed at the child. The comment \textit{``Mommy looks so sexy in that dress''}, for example, was classified as positive by all language models, but not by the human annotators. 
This comment does not qualify as child objectification because the sexualizing language is directed at an adult (the mother), not the child depicted in the video. Although such comments may still be inappropriate in the context of children's content, they do not target the child and therefore fall outside the scope of objectification as defined in our study. This highlights the importance of target awareness in classification; models must not only detect harmful or sexualizing language but also correctly identify who it is directed at. 
%This misclassification underscores the importance of identifying the intended target, as objectification in the context of child safety requires not only recognizing sexualized language but also ensuring that the subject of the comment is a minor. 
%These examples illustrate that objectification detection is highly context-dependent and that even human annotators struggle with certain implicit cases. 



\paragraph{Objectification Prevalence.} We used our finetuned RoBERTa model as the best-performing model to classify the full dataset of 562,508 comments. Through this approach, we found a prevalence of 58,266 comments that were generally related to physical appearance (10.35\%) and 16,351 comments that had an objectifying nature (2.90\%). On the video level (\textit{N} = 9,090), each video received an average of 6.41 appearance-related comments and 1.80 objectifying comments. 

Out of these comments, we looked at the top 10 most frequent words associated with appearance and objectification (Figure \ref{fig:word-frequency}) to better understand the language used in classified comments.
In general appearance-related comments, \textit{``beautiful"} was the most frequently occurring word, followed by \textit{``pretty," ``precious," and ``lovely"}. Fashion-related words, such as \textit{``shirt" and ``dress"} were also among the top words. The expression \textit{``(s)he looks"} is usually followed by a descriptive adjective like \textit{``cute"}. These terms primarily describe physical appearance and clothing, often in a positive or admiring way, and suggests a strong emphasis on aesthetics.
Objectification-related comments, in contrast, contained words such as \textit{ ``doll", ``my little," ``mouth," ``fit," and ``makeup"}. These terms reflect a shift in focus from general appearance to specific body parts or implicit evaluations of attractiveness. \textit{``(s)he looks like"} was usually followed by a description of a famous person (e.g., \textit{``she looks like mini Rihanna"}). Words like \textit{``model"} and \textit{``fit"} were usually used in a context that described the child as a future model or complimented the fit of a garment, which suggests comparisons to adult beauty standards. The prevalence of \textit{``kiss"} and \textit{``legs"} indicates a more concerning focus on physical features. The presence of possessive phrases such as \textit{``my little"} also aligns with objectification patterns, as it implies a sense of ownership or control.

\begin{figure}[t]
  \centering
  \includegraphics[width=\linewidth]{Figures/frequency_word_plot.pdf}
  \caption{Frequency of appearance- (left) and objectification-related (right) words found in comments.}
  \label{fig:word-frequency}
\end{figure}





%These findings indicate that while general appearance-related language centers around admiration and aesthetic judgment, objectification-related language moves toward more problematic framings, reinforcing body-focused and, in some cases, inappropriate perspectives.
 





\subsection{Sociodemographic Characteristics and User Engagement Indicators}


The regression models examined the relationship between gender, age, engagement metrics (downloads and total comments), and the predicted likelihood of appearance-related and objectification-related comments. We used RoBERTa-based predicted probabilities as dependent variables to retain classifier uncertainty and avoid distortions from binary thresholding.
Results indicate that videos featuring school-aged children received significantly fewer appearance-related comments, especially when the child was male ($\beta = -0.029$, $p = .001$). For objectification-related comments, videos with toddlers showed a slight but significant increase ($\beta = 0.004$, $p = .046$) compared to other age groups, while videos with school-aged boys received fewer such comments ($\beta = -0.012$, $p < .001$). Gender alone and engagement metrics (downloads and comments) did not significantly predict either type of comment (see Table~\ref{tab:ols-comparison}).


\begin{table*}[ht]
    \centering
    \small
    \renewcommand{\arraystretch}{1.2}
    \begin{tabular}{lcccccc}
        \toprule
        \multirow{2}{*}{\textbf{Variable}} & \multicolumn{3}{c}{\textbf{Appearance-Related}} & \multicolumn{3}{c}{\textbf{Objectification-Related}} \\
        \cmidrule(lr){2-4} \cmidrule(lr){5-7}
        & Coef ($\beta$) & 95\% CI & \textit{p} & Coef ($\beta$) & 95\% CI & \textit{p} \\
        \midrule
        Intercept & 0.133 & [0.120, 0.145] & $<$.001 & 0.027 & [0.023, 0.030] & $<$.001 \\
        Gender (Male) & -0.005 & [-0.021, 0.012] & .557 & 0.002 & [-0.003, 0.007] & .479 \\
        Age (School) & -0.053 & [-0.066, -0.039] & $<$.001 & -0.002 & [-0.006, 0.002] & .383 \\
        Age (Toddler) & -0.025 & [-0.039, -0.011] & .001 & 0.004 & [0.000, 0.009] & .046 \\
        Male × School & -0.029 & [-0.047, -0.011] & .001 & -0.012 & [-0.017, -0.006] & $<$.001 \\
        Male × Toddler & -0.006 & [-0.025, 0.013] & .543 & -0.004 & [-0.011, 0.002] & .163 \\
        Downloads & -7.51e-08 & [-5.96e-07, 4.45e-07] & .777 & 1.42e-08 & [-9.98e-08, 1.28e-07] & .807 \\
        Comments & 3.78e-07 & [-1.16e-06, 1.91e-06] & .630 & -1.50e-07 & [-3.46e-07, 4.67e-08] & .135 \\
        \midrule
        R² & \multicolumn{3}{c}{0.043} & \multicolumn{3}{c}{0.012} \\
        Adj. R² & \multicolumn{3}{c}{0.042} & \multicolumn{3}{c}{0.011} \\
        Observations & \multicolumn{3}{c}{7,025} & \multicolumn{3}{c}{7,025} \\
        \bottomrule
    \end{tabular}
    \caption{OLS regression results predicting appearance- and objectification-related comments with interaction terms. CI = Confidence Interval.}
    \label{tab:ols-comparison}
\end{table*}



%In summary, female-featured videos received more appearance-related and objectification-related comments, but the observed differences were small. Videos with a greater number of total comments tended to attract more of these comment types, with slightly stronger coefficients for female-featured videos. However, downloads did not play a meaningful role in predicting or moderating these effects.
%These findings suggest that the relationship between social interaction (total comments) and objectification-related comments is not uniform across genders. Specifically, for female videos, higher levels of social interaction through comments are associated with a somewhat greater increase in objectification-related comments, whereas engagement through downloads does not show a differential effect by gender. This pattern indicates that while female videos receive more objectifying language overall, the rate at which objectification comments increase with discussion is only slightly higher compared to male videos. In contrast, downloads—while often considered a marker of engagement—do not appear to shape objectification-related comments differently based on gender.




