Adult content in Social Live Streaming Services: Characterizing deviant users and relationships

TL;DR

This paper analyzes adult content in social live streaming services, revealing ineffective moderation and identifying traits and relationship patterns of deviant users through large-scale data analysis and deep learning.

Contribution

It introduces a deep learning approach to detect adult content broadcasters and provides a comprehensive analysis of deviant user traits and social relationships in SLSS.

Findings

Moderation systems are highly ineffective in suspending adult content users.

Deviant users exhibit distinct traits in social graphs.

Interesting relationship patterns exist among adult content producers and consumers.

Abstract

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.