An Investigation on Multi View Based User Behavior Towards Spam Detection in Social Networks

Online Social Networks have become immensely vulnerable for spammers where they spread malicious contents and links. Understanding the behaviors across multiple features are essential for successful spam detection. Majority of the existing methods rely on single view of information for spam detection where diversified spam behaviors may not allow these techniques to be survived. As a result, Multiview solutions are getting emerged. Based on homophily theory, a hypothesis of spammer’s behaviors should be inconsistent across multiple views compare to legitimate user behaviors is defined. We investigated the consistency of the user’s content interest and popularity over multiple topics across multiple views. The results confirm the existence of notable difference of average similarity between legitimate and spam users. It proved that the legitimate user behaviors are consistent across multiple views while spammers are inconsistent. This indicates that consistency of user behavior across multiple views can be used for spam detection.