# Children's Safety on YouTube: A Systematic Review
## 5 Results and Discussion
One of the most common mental issues a child can acquire through exposure to social media is depression [25, 123]. Another threat that parents encounter is their child's addiction to social media. Kids may develop an addiction as a result of the constant stream of videos, especially on YouTube [18, 21, 124, 125]. The fast-paced, attention-grabbing attitudes of content creators attract the attention of children. Kids often return to these channels/content creators because the uploader usually has an advertised schedule that encourages children to come back and check to see if there have been any new uploads. To put it into perspective, the active community on social media, particularly YouTube, makes children always come back for more. **RQ2**: What existing methods, techniques, and approaches are used to protect kids and prevent potential issues?The aforementioned issues and threats arose due to the increasing popularity of YouTube video watching among children. Several researchers have made many attempts to solve such issues using machine learning approaches early on that required small datasets. Recently, deep and transfer learning approaches contributed to achieving better accuracy in detecting unsafe videos and inappropriate video content. These methods are applied to data collected from the YouTube platform. These data can come in the form of textual data, such as user-generated comments, video descriptions, meta-data, videos in both image and audio, or content analysis methods based on statistical approaches. These attempts concentrate on several methods to provide proper solutions. Some authors analyzed the textual data through different methods. Others suggested slicing the frames of the video and extracting information or identifying unusual patterns for kids in order to detect inappropriate content through using NLP methods for textual data, computer vision methods for video, or transcode that converts the speech to text and then perform further processing by training. Additionally, some went to record to achieve high accuracy in the detection process using the machine, deep, transfer learning models. Therefore, some studies focus on particular languages such as English vocabulary [8; 23; 24; 25; 26; 31; 32; 33; 34; 35; 37; 38; 39; 40; 41; 42; 43; 44; 58], Arabic [28; 29; 36], Portuguese [27; 38], Bengali [30], and Tamil [44]. We noticed that great consideration is given to the English language, as it is a universal language. However, little attention has been given to the Arabic language, despite the fact that it is the fourth most spoken language on the web. Other studies proposed methods to improve the model performance in terms of accuracy [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 39; 41]. Studies [25; 26; 27; 28; 29; 30; 31; 32; 33; 37; 39; 40; 41] used a machine learning classifier, and the best accuracy has been recorded using NB [28; 29; 30; 31], in which accuracy reached 78.5%, 95%, 84%, and 80.57%, respectively, followed by SVM, which achieved 90.3%, 76%, and 66.43%, [26; 30; 43] respectively. As a result of the majority of studies' findings, the NB classifier outperformed other ML classifiers. In addition, ensemble classifiers have been utilized and recorded higher accuracy compared to ML classifiers such as Adaboost, which in studies [25; 32] recorded 91.9% and 97%, and RF in [15; 43] achieved 79% and 66.94%, respectively. Moreover, the best accuracy has been recorded using the deep learning approaches, particularly using RNN and its variants; these studies [26; 37; 39; 40; 41; 42; 43] have obtained a higher accuracy compared to the previously mentioned methods, the accuracy reached 92.8%, 89%, 60%, 90.6%, 86.4%, 96%, 80%, and 74%, respectively. Furthermore, NLP methods and pre-trained models that used word embedding concepts such as Word2 Vec and GloVe were utilized in [27; 33; 34; 35; 37; 39; 41; 42; 44] and outperformed word representation such as BoW and TFIDF that were used in [25; 26; 28; 30; 31; 36; 39; 41; 43].