# Children's Safety on YouTube: A Systematic Review
## 5 Results and Discussion
On the other hand, transfer learning has been used in some of the recent studies [27; 40; 43; 44] by using BERT or models based on BERT. In frame-video analysis, we found that researchers utilized DL more than ML due to its accuracy in detecting inappropriate content. Researchers proposed two types of approaches for detecting inappropriate content in videos. For instance, authors in [1; 2; 3; 5; 6; 8; 10; 11; 13] attempted to create video-related features and used them to build machine learning classifiers using deep learning and transfer learning techniques. They employed features based on video key frames. Transfer learning techniques such as NASNet, EfficientNet-BiLSTM, ResNet 50, InceptionV3, and VGGNet were applied and outperformed deep learning techniques such as YOLOv3 CNN, RNN, and LSTM. Transfer learning techniques outperformed traditional machine learning and deep learning techniques in terms of speed and performance. Because the models used knowledge (features, weights, and so on) from pre-trained models that have already been trained to recognize the features, it is more efficient than training neural networks from scratch. The second category of video frame analysis is based on computer vision techniques [4; 12] in which handcrafted features were extracted by combining SIFT feature descriptors and segmentation techniques with traditional machine learning classification algorithms such as Support Vector Machines to detect skin and non-skin regions. It was also discovered [7] that computer vision techniques combined with CNN and ResNet50 deep learning techniques performed better in detecting inappropriate content in video frames. On the other hand, some research studies highlighted the issue of advertisements that appear suddenly to children while they are watching YouTube videos. These methods are based on content analysis and measure their effectiveness on kids. The research used statistical methods or surveys as tools to identify this phenomenon that is increasing among kids on the YouTube platform. Therefore, the research studies focus on the study of the effects of advertisements on kids [62; 63; 67; 68]. Recently many studies focused on food and brands [64; 65; 70] in order to attract kids. It is one of the many food-marketing strategies. In [69], we highlighted the relation between unhealthy food consumption and YouTube. In addition, some research examines unboxing videos [61]. **RQ3**: What are the challenges in protecting kids from inappropriate content on YouTube? YouTube is considered a tool for education and entertainment. The misuse of this tool negatively affects children. In this digital era, the majority of kids access the internet through different mobile devices. Because to this, families and kids' international organizations that work to protect kids are concerned and encounter great challenges when faced with the question of how to protect kids in the digital world [1; 4; 7; 24; 34; 85; 126; 127; 128; 129; 130]. To achieve this objective and to answer the research question, there are three main challenges to adding a layer to the social media platform. The first is finding a multi-lingual dataset in order to obtain high accuracy in detecting inappropriate content [131; 132; 133; 134; 23]. Such a dataset requires efforts to construct it using a large repository of multi-lingual text that contains inappropriate content by utilizing word synonyms such as WordNet and multi-lingual dictionaries. The second is refining and improving model accuracy by using three or four approaches utilizing deep learning architectures, in addition to using an approach of transfer learning by training deep learning models using the large multi-lingual dataset. Such will improve model performance in terms of accuracy and F1-score to detect and classify inappropriate content. Lastly, for notification purposes, notification should be sent to parents in the event that children access inappropriate content, in accordance with a set of guidelines that includes user-generated comments.