Detecting Spammers with Changing Strategies via a Transfer Distance Learning Method

  • Hao ChenEmail author
  • Jun Liu
  • Yanzhang Lv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


Social spammers bring plenty of harmful influence to the social networking involving both social network sites and normal users. It is a consensus to detect and filter spammers. Existing social spammer detection approaches mainly focus on discovering discriminative features and organizing these features in a proper way to improve the detection performance, e.g., combining multiple features together. However, spammers are easy to escape being detected by using changing spamming strategies. Various spamming strategies bring differences in data distribution between training and testing data. Thus, previous fixed approaches are difficult to achieve desired performance in real applications. To address this, in this paper, we present a transfer distance learning approach, which combines distance learning and transfer learning to extract informative knowledge underlying training and testing instances in a unified framework. The proposed approach is validated on large real-world data. Empirical experiments results give the evidence that our method is efficient to detect spammers with changing spamming strategies.


Transfer distance learning Social spammer detection Spamming strategies 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.National Engineering Lab for Big Data AnalyticsXi’an Jiaotong UniversityXi’anChina
  2. 2.School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina
  3. 3.Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&DXi’anChina

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