Using Bi-level Penalized Logistic Classifier to Detect Zombie Accounts in Online Social Networks

The huge popularity of online social networks and the potential financial gain have led to the creation and proliferation of zombie accounts, i.e., fake user accounts. For considerable amount of payment, zombie accounts can be directed by their managers to provide pre-arranged biased reactions to different social events or the quality of a commercial product. It is thus critical to detect and screen these accounts. Prior arts are either inaccurate or relying heavily on complex posting/tweeting behaviors in the classification process of normal/zombie accounts. In this work, we propose to use a bi-level penalized logistic classifier, an efficient high-dimensional data analysis technique, to detect zombie accounts based on their publicly available profile information and the statistics of their followers' registration locations. Our approach, termed (B)i-level (P)enalized (LO)gistic (C)lassifier (BPLOC), is data adaptive and can be extended to mount more accurate detections. Our experimental results are based on a small number of SINA WeiBo accounts and have demonstrated that BPLOC can classify zombie accounts accurately.