# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Meta-Data Analysis Approach
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Author(s)** & **Language** & **Classes** & **Size** & **Representation** & **Classifiers** & **Accuracy** \\ \hline [PERSON] et al. [58] & Portuguese & positive & & & & & \\  & & negative & & & & & \\  & & neutral & & & & & \\ \hline [PERSON] et al. [39] & English & low, medium, high & 945 movie scripejs & TFDF & SVM & 60\% using RNN (GRU) \\ \hline [PERSON] et al. [40] & N/A & \begin{tabular}{c} produce \\ non produce \\ \end{tabular} & 150 K titles of video streams & TFDF & \begin{tabular}{c} DestilBERT \\ Self-attention model \\ \end{tabular} & 90.6\% \\ \hline [PERSON] et al. [15] & N/A & disturbing and suitable & 2041 videos & \begin{tabular}{c} Features extraction \\ Statistics \\ \end{tabular} & NB, RF, LR and Neural Net & 79\% using RF \\ \hline [PERSON] et al. [51] & English & offensive and non-offensive & 20,000 videos & \begin{tabular}{c} TFDF \\ GloVe \\ Fast Text \\ \end{tabular} & \begin{tabular}{c} LR \\ SVM \\ LSTM \\ Attention Network \\ \end{tabular} & 67.70\% using SVM \\ \hline [PERSON] and [PERSON] [42] & English & bullying and non-bullying & \begin{tabular}{c} Furnspring 12,000 \\ Wikipedia 10,000 \\ Twitter 16,000 \\ YouTube 54,000 \\ \end{tabular} & \begin{tabular}{c} GloVe \\ S8 WE \\ \end{tabular} & CNN, LSTM, BLSTM & \begin{tabular}{c} 90\% using RSTM with attention models (YouTube dataset) \\ \end{tabular} \\ \hline [PERSON] et al. [63] & English & \begin{tabular}{c} isaba vs. ro. ro. (binary) \\ (multi-label) \\ violence, ordered vs. \\ national origin, disability \\ sexual orientation, religion) \\ \end{tabular} & 998 user comments & \begin{tabular}{c} SVM \\ NB \\ LR \\ RF \\ \end{tabular} & 80\% using Dati BERT \\ \hline [PERSON] and & \begin{tabular}{c} non-offensive, offensive- \\ targeted-1-usually-old/dual, \\ offensive-tagged-1-usually-old, \\ offensive-tagged-1-usually-old, \\ offensive-tagged-1-usually-old, \\ offensive-tagged-1-only, \\ other, and \\ non-Termal \\ \end{tabular} & 22,299 user comments & GloVe & \begin{tabular}{c} CNN-BLSTM, \\ mBERT-BLSTM, \\ DstitBERT, XLM-R&BLR, \\ and ULMMT \\ \end{tabular} & 74\% using ULMFFT \\ \hline [PERSON] et al. [58] & English & \begin{tabular}{c} threads, Insists, hate \\ sexually explicit and \\ identify-based attack \\ \end{tabular} & 8276 (pro-NATO) & \
\begin{tabular}{c} 8276 (pro-NATO) \\ 46,46 (anti-NATO) \\ \end{tabular} & Based on score &
\begin{tabular}{c} Analysis and visualization \\ using word cloud \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._[PERSON] and [PERSON] [61] studied toy-unboxing videos to investigate children's participation on YouTube. In toy-unboxing videos, children record themselves reviewing and unboxing toys. The authors analyzed the content of 100 toy-unboxing videos to investigate the children's place in the genres of YouTube. Five categories of these videos were considered: genre, narration, production, product, and branding. The findings in this research indicate that children's production patterns as amateur producers of content are formed by the standardized and common conventions of video genres. In addition, the main findings in the research indicate that there is a relationship between amateur and professional unboxing videos with respect to the methods of filming, editing, narrating, and producing. The authors argued that the mimesis concept is beneficial for attaining insight into a video's popularity among young YouTubers and the collective waves of imitation. Through different approaches to protect and monitor kids on YouTube videos, some researchers focus on the recommendation applications that appear during the videos, along with the inclusion of inappropriate content through advertisements. [PERSON] et al. [23] conducted a search for inappropriate YouTube advertisements or ads for short. They analyzed the advertising patterns of 24.6 K diverse YouTube videos appropriate for young children and found that 9.9% of the 4.6 K unique advertisements shown on these videos contain inappropriate content for young children.