  Social Live Stream Services (SLSS) exploit a new level of social interaction.
One of the main challenges in these services is how to detect and prevent
deviant behaviors that violate community guidelines. In this work, we focus on
adult content production and consumption in two widely used SLSS, namely
Live.me and Loops Live, which have millions of users producing massive amounts
of video content on a daily basis. We use a pre-trained deep learning model to
identify broadcasters of adult content. Our results indicate that moderation
systems in place are highly ineffective in suspending the accounts of such
users. We create two large datasets by crawling the social graphs of these
platforms, which we analyze to identify characterizing traits of adult content
producers and consumers, and discover interesting patterns of relationships
among them, evident in both networks.
