Leveraging Behavior Diversity to Detect Spammers in Online Social Networks

  • Jian Cao
  • Qiang Fu
  • Qiang LiEmail author
  • Dong Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9530)


Online social networks have become very popular and convenient for communication. However, spammers often take control of accounts to create and propagate attacks using messages and URLs. Most existing studies to detect spammers are based on machine learning methods. Features are the key factors considered in these methods, and most documented features in existing studies can be evaded by spammers. In this study, we propose behavior features, which are based on behavior diversity when sending messages, combined with existing effective features, to build a detection system. We leverage entropy to present differences in behavior diversity between spammers and normal accounts. In the cases of evasion by periodically changing a behavior model in the sending of messages by spammers, we also introduce conditional entropy, which is calculated based on the Markov model. To achieve our goal, we have collected information from approximately 489,451 accounts including 108,168,675 corresponding messages from Sina Weibo. Through evaluation of our detection methods, the accuracy rate of this system is approximately 91.5 %, and the false positive rate is approximately 3.4 %.


Online social network Spammer detection Behavior diversity Entropy Conditional entropy 



This work is supported by the National Natural Science Foundation of China under Grant No. 61170265 and Grant No.61472162.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyJilin UniversityChangchunChina

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