Abstract
Users of social media sites can use more than one account. These identities have pseudo anonymous properties, and as such some users abuse multiple accounts to perform undesirable actions, such as posting false or misleading remarks comments that praise or defame the work of others. The detection of multiple user accounts that are controlled by an individual or organization is important. Herein, we define the problem as sockpuppet gang (SPG) detection. First, we analyze user sentiment orientation to topics based on emotional phrases extracted from their posted comments. Then we evaluate the similarity between sentiment orientations of user account pairs, and build a similar-orientation network (SON) where each vertex represents a user account on a social media site. In an SON, an edge exists only if the two user accounts have similar sentiment orientations to most topics. The boundary between detected SPGs may be indistinct, thus by analyzing account posting behavior features we propose a multiple random walk method to iteratively remeasure the weight of each edge. Finally, we adopt multiple community detection algorithms to detect SPGs in the network. User accounts in the same SPG are considered to be controlled by the same individual or organization. In our experiments on real world datasets, our method shows better performance than other contemporary methods.
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References
Andrew M. Whole foods executive used alias. New York Times, 2007, 12
Lea R, Taylor M. Historian Orlando Figes admits posting Amazon reviews that trashed rivals. The Guardian, 2010
Eligon J. Dispute over dead sea scrolls leads to a jail sentence. New York Times, 2010
Olivier D, Anderson A, Corney M, Mohay G. Mining e-mail content for author identification forensic. ACMSIGMOD Record, 2001, 30(4): 55–64
Corney M. Analyzing e-mail text authorship for forensic purpose. Mas ter’s Thesis Australia: University of Software Engineering and Data Communications, 2003
Abbasi A, Chen H.Writeprints: a stylemetric approach to identity-level identification and similarity detection in cyberspace. ACM Transaction on Information System, 2008, 26(2): 14–24
Revett, K. Behavioral Biometrics: A Remote Access Approach. Chichester: John Wiley & Sons, 2008
Gao H, Hu J, Wilson C, Li Z C, Chen Y, Zhao B. Detecting and characterizing social spam campaigns. In: Proceedings of the Internet Measurement Conference. 2010, 35–47
Thomas K, Grier C, Ma J, Paxon Y. Design and evaluation of a realtime URL spam filtering service. In: Proceedings of the 32nd IEEE Symposium on Security and Privacy. 2011
Yang C, Harkreader R, Zhang J, Shin S, Gu G. Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 71–80
Newman M, Girvan M. Finding and evaluating community structure in networks. Physical Review E, 2004, 69(2): 26113
Fortunato S, Latora V, Marchiori M. A Method to find community structure based on information centrality. Physical Review E, 2004, 70(5): 056104
Rosvall M and Bergstrom C. An information-theoretic framework for resolving community structure in complex networks. Proceedings of the National Acadmy of the United States of America, 2007, 104(18): 7327–7331
Solorio T, Hason R, Mizan M. A case study of sockpuppet detection in wikipedia. In: Proceedings of the Workshop on Language Analysis in Social Media. 2013, 59–68
Thamar S, Ragib H and Mainul M. Sockpuppet detection in wikipedia: a corpus of real-world deceptive writing for linking identities. In: Proceedings of the 9th International Conference on Language Resources and Evaluation. 2014, 26–31
Zheng X, Lai Y, Chow K, Hui L C K, Yiu S M. Sockpuppet detection in online discussion forums. In: Proceedings of the 7th International Conference on Intelligent Information Hiding and Multimedia Signal Processing. 2011, 374–377
Bu Z, Xia Z and Wang J. A sock puppet detection algorithm on virtual spaces. Knowledge-Based Systems, 2013, 37:366–377
Ding X, Liu B and Philip Y. A holistic lexicon-based approach to opinion mining. In: Proceedings of the International Conference on Web Search and Web Data Mining. 2008, 231–240
Gregory S. Finding overlapping communities in networks by label propagation. In: Proceedings of the 1st International Workshop on Complex Networks. 2009, 47–61
Blondel V, Guillaume J, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, 1–12
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Dong Liu received his MS in Computer Science from National University of Defense Technology, China. He is currently pursuing his PhD from the same university. His research interests include network and information security, social network analysis, and data mining.
Quanyuan Wu graduated from FuDan University, China. He is currently a professor, and PhD supervisor in the School of Computer, National University of Defense Technology, China. His main research interests include artificial intelligence, expert systems, and distributed computing.
Weihong Han received her PhD from National University of Defense Technology (NUDT), China. She is currently a professor, and PhD supervisor in the School of Computer of NUDT. Her research interests include network and information security, database and data mining.
Bin Zhou is a professor and PhD supervisor in the School of Computer, National University of Defense Technology, China. His research interests include network and information security, social network analysis, and data mining.
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Liu, D., Wu, Q., Han, W. et al. Sockpuppet gang detection on social media sites. Front. Comput. Sci. 10, 124–135 (2016). https://doi.org/10.1007/s11704-015-4287-7
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DOI: https://doi.org/10.1007/s11704-015-4287-7