PUED: A Social Spammer Detection Method Based on PU Learning and Ensemble Learning

Abstract

In social network, people generally tend to share information with others, thus, those who have frequent access to the social network are more likely to be affected by the interest and opinions of other people. This characteristic is exploited by spammers, who spread spam information in network to disturb normal users for interest motives seriously. Numerous notable studies have been done to detect social spammers, and these methods can be categorized into three types: unsupervised, supervised and semi-supervised methods. While the performance of supervised and semi-supervised methods is superior in terms of detection accuracy, these methods usually suffer from the dilemma of imbalanced data since the number of unlabeled normal users is far more than spammers’ in real situations. To address the problem, we propose a novel method only relying on normal users to detect spammers exactly. We present two steps: one picks out reliable spammers from unlabeled samples which is imposed on a voting classifier; while the other trains a random forest detector from the normal users and reliable spammers. We conduct experiments on two real-world social datasets and show that our method outperforms other supervised methods.