# Children's Safety on YouTube: A Systematic Review
## 4 Approaches and Existing Methods
### Meta-Data Analysis Approach
[67] stated that the content of videos is not restricted by federal policy, especially on free video platforms. The authors identified the ten most popular kids' channels on YouTube for content analysis. Five videos with the highest view counts were watched to ensure that these videos were child-directed. The duration of ads and the proportion of inappropriate ads in these videos were noted. They concluded that the total number of ads in the 50 videos was 286. The sidebar ads were the most popular ones at 41.6%, followed by pre-roll ads at 16.8%, banner ads at 14.7%, interstitial ads at 14.3%, and post-video ads at 12.6%. This study was limited to English-language videos. [PERSON] et al. in [68] studied the effect of advertisements posted on services targeting kids on YouTube and investigated the demographics of users. To achieve this, data from 12,848 videos were collected from 24 channels in Brazil and 17 channels in the UK and the USA. The collected data included video statistics, comments, and replies. The number of comments collected from these channels was more than 14 million. The authors utilized a free tool for face recognition and text analysis to identify the race, gender, and age of users and to characterize their behavior. Moreover, Named Entity Recognition (NER), with the assistance of video meta-data, was used to extract entities such as brands, products, names, etc. The sentiment analysis concept was used to retrieve the public perception of videos published by analyzing the comments. The children's activities were identified, and this showed that the minimum age of kids utilizing the YouTube platform is 13 years. The study illustrates that the proportion of Black users was very small when compared to Asian and White users. [PERSON] et al. [69] examined the impact of online behavior and social media on unhealthy food consumption by children. The authors carried out an online survey targeting ten- to sixteen-year-old children. The number of children invited to participate in the questionnaire was 582, and 417 responses were received. The analysis of these responses showed that 304 children utilized social networking platforms. Fifty-two children stated watching their favorite food brands on social media ads. Additionally, 25 children reported that food brands are seen in social media hashtags. The number of children that watched YouTube was 329. The main finding of this study was that consuming unhealthy foods and beverages is closely associated with social media behavior. Owing to watching YouTube ads, children were encouraged to purchase unhealthy food. [PERSON] et al. [70] identified young influencer practices on YouTube and the existence of brands in the posted content. To achieve this, the authors selected the top five most-subscribed channels targeting children. For each selected channel, the five most-viewed videos were selected to conduct content analysis, and each video lasted up to 15 min. Several aspects were studied, and each of them consisted of indicators. These aspects were: promotion related to viewers' participation, interaction type, speaking style, editing content complexity, brand content analysis, and the usage of an introduction and outroduction. [PERSON] et al. in [18] proposed a classifier with the ability to recognize inappropriate and unsuitable content intended for kids on YouTube. The authors collected and reviewed child-oriented videos manually and labeled them into one of four categories: suitable, irrelevant, restricted, and disturbing. The collected dataset included 1513 suitable,419 restricted, 929 disturbing, and 1936 irrelevant videos. The authors collapsed the initial labels of the videos into two categories to distinguish inappropriate from appropriate videos and obtain an accurate classifier. To detect disturbing videos, a deep learning classifier was developed aiming to enhance the performance compared to the baseline models. Several features were utilized in the developed classifier, including title, tags, thumbnail images, statistical meta-data, and style features.