Spammer Detection on Online Social Networks

Abstract

Twitter with its rising popularity as a micro-blogging website has inevitably attracted attention of spammers. Spammers use myriad of techniques to lure victims into clicking malicious URLs. In this thesis, we present several novel features capable of distinguishing spam accounts from legitimate accounts in real-time. The features exploit the behavioral and content entropy, bait-techniques, community-orientation, and profile characteristics of spammers. We then use supervised learning algorithms to generate models using the proposed features and show that our tool, spAmbush, can detect spammers in real-time. Our analysis reveals detection of more than 90% of spammers with less than five tweets and more than half with only a single tweet. Our feature computation has low latency and resource requirement. Our results show a 96% detection rate with only 0.01% false positive rate. We further cluster the unknown spammers to identify and understand the prevalent spam campaigns on Twitter.