Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this paper, we present a novel spammer classification approach based on Latent Dirichlet Allocation (LDA), a topic model. Our approach extracts both the local and the global information of topic distribution patterns, which capture the essence of spamming. Tested on one benchmark dataset and one self-collected dataset, our proposed method outperforms other stateof-the-art methods in terms of averaged F1score.
['Linqing Liu', 'Yao Lu', 'Ye Luo', 'Renxian Zhang', 'Laurent Itti', 'Jianwei Lu']
Detecting "Smart" Spammers On Social Network: A Topic Model Approach