# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Textual Analysis Approach (TA)
In this approach, the model required more training to achieve higher accuracy. In the same way as other researchers, they utilized machine learning classifiers using a publicly available Arabic dataset collected from a YouTube video. [PERSON] and [PERSON] [28] used three classifiers: multinomial NB, complement NB, and LR classifiers, to compare their performance in automatically detecting cyberbullying in two different feature representations using TFIDF and count vectorizer. The results show that LR outperforms the other two classifiers in vectorizer, which achieved 78.6% in F1-score, while complement NB recorded 78.5%. [PERSON] et al. [29] used 25,000 user comments from YouTube and Twitter and applied an NB classifier after several NLP pre-processing steps. The NB recorded 95% in terms of accuracy in detecting inappropriate information. The Bengali language is used in [30,31], and [PERSON] et al. [30] utilized classifiers SVM, LR, NB, and ensemble methods (XGBoost). The dataset is based on the Bengali language collected from YouTube user comments and other sources. They built three datasets--the first dataset consisted of 5000 Bengali texts, the second dataset consisted of 7000 randomized texts, and a combination of the two sets consisted of a total of 12,000 user comments. These were divided into two classes: bullying and non-bullying based on the three datasets. Before, experiments were carried out with several NLP tasks, which were performed to prepare the dataset for ML and ensemble classifiers on three datasets. The model achieved an accuracy of 76%, 84%, and 80% using the first dataset with SVM, the second dataset with NB, and the third dataset with NB. [PERSON] et al. [31] applied NB in the Bengali language that was collected from YouTube and translated from English language. The dataset had 2665 user comments in the form of two classes: abusive and non-abusive. Ensemble methods [32] are used to detect cyberbullying based on personality traits through collected user comments from YouTube and applied the classifiers, which achieved 95% model accuracy. All the aforementioned research is used to detect inappropriate content based on two classes (binary classes), and there are research works made attentively by studying the same issue from different perspectives. Therefore, the authors in [4,33-35] proposed a variety of methods to detect inappropriate content in user comments by the affected range of kids and examine URLs in user comments. The authors collected 3.7 million comments posted on children's videos. Then, in [33], the user comments were divided into five groups based on age. They found that the group aged 13-17 was the most exposed to inappropriate comments, followed by the 6-8 age group. In [4], [PERSON] et al. studied the presence of inappropriate and malicious content in comments of YouTube videos targeting kids and teenagers by investigating the existence of malicious and inappropriate URLs found inside the comments posted. Among these comments, 8677 URLs were extracted using a regular expression. Moreover, 107 various topic categories associated with these URLs were extracted. The top ten categories included \"Entertainment\", \"Television\", \"Streaming Media\", \"Society\", \"Social Networking\", \"News/Weather\", \"Technology\", \"Music\", \"Hobbies\", and \"File Sharing\". The authors stated that the URLs in comments in videos targeting kids are very likely to be blindly clicked. Finally, the authors checked the validity of a given URL by accessing them and investigating the response of HTML requests. [PERSON] et al. [35] studied the detection of toxic behaviors presented in YouTube video comments and new video captions. They collected more than 7.3 million comments posted in news videos and 10,000 captions on those videos. Two datasets were utilized for training the ensemble classifier: Wikipedia Ground Truth and YouTube Ground Truth datasets. The former dataset consists of 160,000 comments manually annotated, with 15,294 labeled as toxic, 143,000 safe, 1405 hate, and 8449 obscene.