Social Spammer Detection via Structural Properties in Ego Network

Social media have become popular communication platforms in recent years. A huge number of users disseminate and share information on these websites. Due to their popularity, social media have attracted numerous malicious users (spammers) to send spams, spread malware and phish scams. It is highly desirable to automatically distinguish legitimate users from spammers. Existing approaches mainly use behavior, content, or profile information as features to characterize the social spammers. However, to avoid being caught by the websites, the spammers pretend to post normal messages sometimes and change their behaviors continuously. This makes the behavior and content based approaches less effective.