# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Video Frame Analysis Approach  
Video content analysis is concerned with the extraction of meta-data from raw video to be utilized as features in applications such as event detection, tracking, search, clas \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author(s)** & **Dataset** & **Language** & **Parameters** & **Class(s)** & **Finding** \\ \hline [PERSON] et al. & 4797 & N/A & title, tags, thumbnail images, statistical meta-data, and style features & Suitable, irrelevant, restricted, and disturbing & Proposed DL classifier using LSTM, accuracy 84\% \\ \hline [PERSON] [60] & 330,925 videos & English & number of subscribers and views, comments captions & Alt-life & Proposed recommendation algorithm \\  & & & & & \\  & & & & & \\ \hline [PERSON] and [PERSON] [61] & 100 videos & N/A & statistics analysis & Genre, narration, production, product, branding & Analyzing children’s toy-unboxing videos \\ \hline [PERSON] and [PERSON] [62] & N/A & N/A & analysis of the existing regulation & N/A & Strategies affect kids’ health \\ \hline [PERSON] et al. [64] & 250 videos & N/A & watching videos & Calculating the advertising in videos & Advertising about unhealthy foods \\ \hline [PERSON] and [PERSON] [65] & 304 videos & Spanish & watching videos & Content analysis & Food brands marketing through YouTube \\ \hline [PERSON] [66] & 41 channels & Spain & statistics using SPSS, parameters country, number of subscribers, number of travelling area & Structuring, editing, secondary, social media usage, and statistics & Analysis YouTube channel (educational channels of sciences) \\ \hline [PERSON] et al. [67] & 5 videos & English & watching videos & Calculating the duration of the advertising video \\ \hline [PERSON] et al. [68] & 12,848 videos & English & list of channels, video statistics, comments, and replies & Content analysis & Impact of advertising \\ \hline [PERSON] et al. [69] & 417 responses & N/A & purchasing unhealthy food online & Survey response & Survey about advs. on YouTube \\ \hline [PERSON] et al. [71] & 5 channels & Spanish & watching videos & Content analysis & Presence of brands on YouTube videos \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative study on meta-data analysis. sification, and summarization. It has gained much attention in recent years because of rapid advances in artificial intelligence and deep learning techniques. Furthermore, several studies investigated specific video content classification that could be used for classifying content appropriate for children. For example, violent scenes could be automatically detected by the presence of gunshots, blood, explosions, hate speech, alcohol, drug, and aggressive human actions such as hitting, kicking, punching, slapping, and screaming in audio or video signals. In this approach, researchers gave more attention to detecting and filtering inappropriate YouTube videos that can impact kids by extracting the image frame from the video. Then, features that contain inappropriate content for kids are extracted. Following is a detailed review of recent related work on video content analysis for detecting inappropriate content in videos. [PERSON] et al. [71] developed deep learning-based approaches for automatically extracting discriminative space-temporal information for filtering \"Elsagate\" content from disturbing cartoons. They extracted the static information from the videos by sampling one frame per second, and the temporal information was extracted from MPEG motion vectors. Four different deep learning architectures--SqueezeNet, MobileNetV2, GoogLeNet, and NASNet--were evaluated on a public dataset for \"Elsagate\" classification problems. After processing static and motion data using an SVM classifier, late fusion was finally applied, yielding independent classification scores that were combined to create a single score for the final classification. They claim that when applying transfer learning from ImageNet, NASNet performed better and was a more effective feature extractor.