A Multi-Perspective Approach for Risky User Identification in Social Networks

Social networks have become an integral part of modern communication, allowing people to connect and interact across the globe. However, they also bring along some negative phenomena, such as cyberbullying and social media addiction. As a result, monitoring user behavior and content has become essential to ensure a safe and responsible use of social networks. In this context, we recently proposed a novel system called SAIRUS, that we describe in this discussion paper. SAIRUS adopts three separate models to learn from multiple perspectives of social network data, namely the content posted by users, their relationships and their spatial closeness. We compare the system performance with 13 competitors on two real world datasets, demonstrating its superiority in identifying risky users and its usefulness as a tool for social network analysis.