\section{Methods}

\subsection{Video Collection and Annotation}
As TikTok’s user guidelines prohibit individuals under 13 from holding their own accounts \citep{tiktok2024}, this study does not examine accounts run directly by minors. Instead, we focused on accounts that feature children under 13 but are managed by adults—usually their parents.
To create our dataset, we began by identifying TikTok accounts with the highest follower counts.
%An initial search revealed that many of these accounts did not feature children at all, while some had restricted access or private videos. 
To ensure a substantial and representative sample of approximately 500 TikTok accounts, we initially screened 25,000 of the most-followed accounts. %This was done by analyzing automatically generated screenshots of the first page of their video feeds.
We identified 825 accounts that regularly featured children and collected the first 100 videos from each. Both the videos and their comments were collected using the Ensemble Data TikTok API between November and December 2024.


A research team consisting of two postdoctoral researchers and two research assistants then reviewed the collected videos (\textit{n} = 82,500) to identify those featuring a child under the age of 13. We relied on visual cues (e.g., toddlers), and when uncertain, we used any age information provided in TikTok videos or profile descriptions. Given the high volume of videos, we manually labeled a subset of 12,000 videos and trained a neural network classifier to detect children based on image frames. Our model achieved an accuracy of 93\% (see \nameref{sec:app}, Section \ref{app-video-classification} for details). We manually reviewed the videos again during a final annotation stage and could thus correct misclassifications. To ensure meaningful representation, our final dataset includes only accounts with at least 20 videos featuring children. 
In the final annotation round, we annotated the child's perceived gender, categorizing each instance as female, male, or having children of multiple genders present (mixed). When there were no visual cues indicating gender, we labeled the gender as other.\footnote{We acknowledge that gender identity is diverse. Our use of visual cues or descriptions is based on conventional perceptions and does not intend to exclude or invalidate non-binary, genderqueer, or other gender identities.} Age groups were similarly coded as infant, toddler, or school-aged.

\begin{table}[ht]\small
\centering
\begin{tabular}{lll}
\toprule
\textbf{Group} & \textbf{Videos} & \textbf{Comments} \\
\midrule
\addlinespace[1ex]
\multicolumn{3}{l}{\textit{Child Gender}} \\
\cmidrule(lr){1-1}
Female       &   4,289 (47.18\%) &  259,705 (46.17\%) \\
Male         &   3,346 (36.81\%) &  204,392 (36.34\%) \\
Other        &     280 (3.08\%)  &   12,677 (2.25\%)  \\
Mixed        &   1,145 (12.60\%) &   84,920 (15.10\%) \\
\addlinespace[1ex]
\midrule
\addlinespace[1ex]
\multicolumn{3}{l}{\textit{Child Age Group}} \\
\cmidrule(lr){1-1}
Infant       &   1,766 (19.43\%) &   94,056 (16.72\%) \\
Toddler      &   3,000 (33.00\%) &  171,560 (30.50\%) \\
School-aged  &   2,995 (32.95\%) &  207,777 (36.94\%) \\
Mixed        &   1,300 (14.30\%) &   88,289 (15.70\%) \\
\addlinespace[1ex]
\midrule
\textbf{Total} &  \textbf{9,090} (100.00\%) &  \textbf{562,508} (100.00\%) \\
\bottomrule
\end{tabular}
\caption{Overview of the TikTok Children Dataset. Absolute counts and percentages (in brackets) are provided to show the distribution of videos and their corresponding comments by child gender and age group. Mixed refers to videos featuring children of different perceived genders or age groups being featured in one video.}
\label{tab:summary}
\end{table}



\subsection{Comment Collection and Classification}
\label{sec:meth_comm}
For each video, we collected up to the first 300 comments, along with associated video metadata.
To ensure consistency and reliability in the analysis, we included only English-language comments based on TikTok’s metadata. Focusing on English as the most widely used language on the platform helped reduce linguistic variation and improved the generalizability of our findings. The final dataset includes 562,508 comments from 9,090 unique videos across 482 TikTok accounts (Table \ref{tab:summary}). These accounts are distributed across 21 countries (see \nameref{sec:app}, Section \ref{app:geo} for details). 





%To answer our research questions on appearance-related language in comments on videos picturing children, we first looked at the overall context for a broader picture, defined categories of our Child Objectification Language Typology, and then analyzed how we can train different language models on an automated detection of these categories.

\paragraph{Topic Modeling.}
Addressing RQ1 on the linguistic context of how children are discussed on TikTok, we first examined whether appearance-related language was prevalent enough in our dataset to justify applying our theoretically motivated typology to TikTok comments. 
%While previous research suggests that objectification is a relevant factor in online discourse, it is not immediately clear whether it appears in TikTok comments in a way that supports a structured classification approach. 
To investigate this, we conducted topic modeling using BERTopic \citep{grootendorst2022bertopic} to identify common topics. 
%This topic modeling technique relies on embeddings retrieved through the language model Bidirectional Encoder Representations from Transformers (BERT) \citep{devlin-etal-2019-bert} and Term Frequency-Inverse Document Frequency (TF-IDF) to cluster semantically similar comments. Each comment was transformed into a vector representation using pre-trained BERT embeddings. Before training the topic model, we excluded person names to ensure a more meaningful representation of thematic structures. 
The extracted topics were visualized using UMAP (Uniform Manifold Approximation and Projection).%, a dimensionality reduction algorithm that preserves local structure while mapping high-dimensional embeddings into a two-dimensional space. 


\paragraph{Implementing the Objectification Typology.}

To implement our Child Objectification Language Typology (RQ2), we developed two distinct classifiers for both general appearance-related and specifically objectifying comments according to the typology (see \nameref{sec:app}, Figure \ref{app:pipeline}). For our training data, two postdoctoral researchers and one research assistant labeled a sample of 6,000 comments according to our typology. We calculated Fleiss' Kappa for inter-annotator agreement \citep{fleiss1971}, yielding moderate agreement with scores of $\kappa=.74$ for general appearance-based comments and $\kappa=.63$ for the objectification category. After the initial round of agreement checks, any ambiguous cases were discussed within the research team, and final labels were assigned collaboratively. Given the inherently subjective nature of interpreting objectification in social media comments, some disagreement was expected. While not perfect, this level of agreement is comparable to tasks like implicit hate speech detection \citep{li2024hot, matter2024investigating}. 
%While the level of agreement was not perfect, it offers a strong foundation for the study, with room for refinement in future work.


\paragraph{Model Selection.}  
We implemented five classification models, evaluating their suitability for detecting objectification-related language in TikTok comments. %For each model, we developed two distinct classifiers: one for detecting objectification-related language and another for identifying general appearance-related descriptions. 
%The selection of these models was guided by key criteria such as complexity, interpretability, hyperparameter optimization, and scalability. 
Hyperparameter details for each model are included in the \nameref{sec:app}, Section \ref{app:hyper}. During training, we used a 5-fold cross validation and a classification threshold of 0.5 for prediction. We evaluated each model using F1-score (binary), precision, recall, and AU-ROC.
  

\textbf{N-Gram Neural Network Model.}  
We implemented a fully connected neural network (NN) trained on n-gram representations, where \textit{n} = [1, 2, 3]. In this model, the input text is transformed into term frequency representations of n-grams, which are then passed through dense feed-forward layers with non-linear activations. This model balances interpretability with expressive power, capturing both lexical patterns and basic phrase structures while remaining computationally efficient. 

\textbf{RoBERTa (finetuned).}  
To incorporate deep contextual representations, we used RoBERTa \citep{liu2019roberta}. %a transformer-based model designed to enhance contextual learning through masked language modeling. 
We fine-tuned RoBERTa on our dataset, optimizing key hyperparameters including learning rate and batch size. RoBERTa’s strength lies in its ability to capture subtle linguistic patterns and implicit biases, making it useful for detecting indirect forms of objectification in online discourse.

\textbf{Large Language Models (GPT-4, LLaMA 2, and Mistral 7B).} 
To evaluate the capabilities of large-scale generative models, we included GPT-4 \citep{openai2024gpt} via OpenAI's API and open-source models LLaMA 2 \citep{touvron2023llama} and Mistral 7B \citep{jiang2023mistral} in both zero-shot and few-shot settings. These models process classification as a text generation task, where we reframe objectification detection as a structured prompt-based task. 
%GPT-4, accessed via OpenAI’s API, provides strong contextual understanding and adaptability, its use is constrained by cost, limited transparency, and reliance on API access. LLaMA 2, developed by Meta, is an open-source model designed for efficiency and scalability, allowing greater flexibility in deployment without API restrictions. Mistral 7B, another open-source transformer model, is optimized for efficiency while maintaining competitive NLP performance. 
Each prompt included instructions to either classify general appearance-related comments or objectifying comments targeted towards children. For the objectification classification task, we included the categories of our typology in both zero- and few-shot prompts (see \nameref{sec:app}, Section \ref{app:prompts}).




\subsection{Sociodemographic Characteristics and User Engagement Indicators} 
To analyze the relationship between gender, age, engagement metrics, and the prevalence of appearance-related and objectification-related comments (RQ3), we employed Ordinary Least Squares (OLS) regression. We estimated separate models for appearance-related and objectification-related comments, using the predicted probabilities of each comment (aggregated at the video level) as the dependent variables. Independent variables included gender, age group, and their interaction, along with the number of downloads and total comments to account for engagement.




