# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Textual Analysis Approach (TA)
In an effort to emphasize the existing unsafe nature of the YouTube Kids channel, [PERSON] et al. [15] studied the new \"made for kids\" feature. They were also the first to study the characteristics of YouTube accounts publishing videos made for kids. They discovered that 25% of channels with suitable content are set to \"made for kids\", while only 3% of channels with inappropriate content are set as such. They analyzed 27 different characteristics of channels and how these features are associated with the type of channel and the content it publishes. Among these features are country and channel creation date, statistics such as subscriptions and video views, keywords and topics, social media links, polarity, and sentiment of description. They applied several experiments using MLC, such as NB, RF, LR, and Neural Net. The RF classifier outperformed them all with an accuracy rate of 79%. It outperformed all the other classifiers in TPRate, Precision, Recall, F1, and AUC. However, it struggled heavily in FPRate, scoring the lowest result out of all the other classifiers. These experiments carried out on the dataset consisted of a set of 2041 random videos from 1338 YouTube channels, which were then placed into two categories: disturbing and suitable. In [41], [PERSON] proposed a system in order to identify inappropriate content on YouTube by using machine learning classifiers and deep learning architectures. They started by building an offensive dictionary that contained harsh terms that were used to retrieve YouTube videos through the YouTube API. It was used to detect offensive YouTube comments and then rank them on a scale of 0-100% for how inappropriate they were, based on the sentiment analysis concept. Their dataset consisted of 20 k+ videos based on keywords and extracted all the critical information available and categorized them into offensive and non-offensive. They applied several experiments with various combinations of features sentiments from comments, thumbnail images, text-based features, and video and frame-level features using SVM, CNN, LR, and LSTM. The model achieved the highest accuracy, reaching 86.4% by using GRU and Hierarchical Attention Network with GloVec. [PERSON] and [PERSON] in [42] examined DL models on different cyberbullying datasets collected from Twitter, Wikipedia, Formspring, and YouTube. Additionally, they applied different feature representation methods through embedding, such as SSWE and GloVec. The experiment shows that BLSTM with attention models outperforms all other classifiers using the same datasets. The model accuracy achieved is 96% using the YouTube dataset. [PERSON] et al. [43] propose two datasets, one for binary classification, which consists of 998 user comments collected from YouTube videos, while the other contains 433 user comments of multi-classes. These datasets can be used for detecting inappropriate content. To assess the proposed datasets, several ML classifiers LR, SVM, RF, and NB were used. Additionally, Gradient Boosting, several deep learning architectures such as CNN and BiLSTM, and transfer learning concepts such as BERT and DistilBERT were also used. The best accuracy rate was achieved through DistilBERT, which reached 80%. In the same way, [PERSON] and [PERSON] [44] examined the detection of offensive content/speech on YouTube based on textual data from multiple languages while concentrating on Tamil-English. They used a public dataset extracted from 22,299 user comments on YouTube. Then, they divided it into six classes: non-offensive, offensive--targeted-insult-individual, offensive--targeted-insult-group, offensive--targeted-insult-other, offensive--untargeted, and not-Tamil. The dataset is considered an imbalanced dataset. They applied it as a server extensive experiment using multiple deep learning and transfer learning models from HuggingFace, such as CNN-BiLSTM, mBERT-BiLSTM, DistilBERT, XLM-RoBERTa, and ULMFiT. The best-performing model was ULMFiT, which had a recorded accuracy of 74%.