  Social media has become an essential part of the daily routines of children
and adolescents. Moreover, enormous efforts have been made to ensure the
psychological and emotional well-being of young users as well as their safety
when interacting with various social media platforms. In this paper, we
investigate the exposure of those users to inappropriate comments posted on
YouTube videos targeting this demographic. We collected a large-scale dataset
of approximately four million records and studied the presence of five
age-inappropriate categories and the amount of exposure to each category. Using
natural language processing and machine learning techniques, we constructed
ensemble classifiers that achieved high accuracy in detecting inappropriate
comments. Our results show a large percentage of worrisome comments with
inappropriate content: we found 11% of the comments on children's videos to be
toxic, highlighting the importance of monitoring comments, particularly on
children's platforms.
