Discovering social spammers from multiple views.pdfVIP

Neurocomputing 225 (2017) 49–57 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom Discovering social spammers from multiple views Hua Shena,d, Fenglong Mac, Xianchao Zhangb,?, Linlin Zonga,b, Xinyue Liub, Wenxin Liangb a School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China b School of Software, Dalian University of Technology, Dalian 116620, China c SUNY at Bu?alo, Bu?alo, NY 14221, USA d College of Mathematics and Information Science, Anshan Normal University, Anshan 114007, China MARK ARTICLE INFO Communicated by Peng Cui Keywords: Social spammer detection Multi-view learning Social regularization term ABSTRACT Online social networks have become popular platforms for spammers to spread malicious content and links. Existing state-of-the-art optimization methods mainly use one kind of user-generated information (i.e., single view) to learn a classi?cation model for identifying spammers. Due to the diversity and variability of spammers strategies, spammers behavior may not be completely characterized only by single views information. To tackle this challenge, we ?rst statistically analyze the importance of considering multiple view information for spammer detection task on a large real-world Twitter dataset. Accordingly, we propose a generalized social spammer detection framework by jointly integrating multiple view information and a novel social regularization term into a classi?cation model. To keep the completeness of the original dataset and detect more spammers by the proposed method, we introduce a simple strategy to ?ll the missing data for each view. Experimental results on a real-world Twitter dataset show that the proposed method outperforms the existing methods signi?cantly. 1. Introduction Online social networks (OSNs), such as Twitter and Facebook, have become popular platforms to disseminate and share information [1]. Unfortunately, social spammers take advantage of thos