Uncovering social spammers: social honeypots + machine learning

Web-based social systems enable new community-based opportunities for participants to engage, share, and interact. This community value and related services like search and advertising are threatened by spammers, content polluters, and malware disseminators. In an effort to preserve community value and ensure longterm success, we propose and evaluate a honeypot-based approach for uncovering social spammers in online social systems. Two of the key components of the proposed approach are: (1) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities; and (2) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers. We describe the conceptual framework and design considerations of the proposed approach, and we present concrete observations from the deployment of social honeypots in MySpace and Twitter. We find that the deployed social honeypots ident...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Kyumin Lee, James Caverlee, Steve Webb
• Keywords: Information Management, SIGIR 2010, Social, Social Honeypots, Social Spammers