  YouTube draws large number of users who contribute actively by uploading
videos or commenting on existing videos. However, being a crowd sourced and
large content pushed onto it, there is limited control over the content. This
makes malicious users push content (videos and comments) which is inappropriate
(unsafe), particularly when such content is placed around cartoon videos which
are typically watched by kids. In this paper, we focus on presence of unsafe
content for children and users who promote it. For detection of child unsafe
content and its promoters, we perform two approaches, one based on supervised
classification which uses an extensive set of video-level, user-level and
comment-level features and another based Convolutional Neural Network using
video frames. Detection accuracy of 85.7% is achieved which can be leveraged to
build a system to provide a safe YouTube experience for kids. Through detailed
characterization studies, we are able to successfully conclude that unsafe
content promoters are less popular and engage less as compared with other
users. Finally, using a network of unsafe content promoters and other users
based on their engagements (likes, subscription and playlist addition) and
other factors, we find that unsafe content is present very close to safe
content and unsafe content promoters form very close knit communities with
other users, thereby further increasing the likelihood of a child getting
getting exposed to unsafe content.
