ENWalk: Learning Network Features for Spam Detection in Twitter

  • K. C. SantoshEmail author
  • Suman Kalyan Maity
  • Arjun Mukherjee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10354)


Social medias are increasing their influence with the vast public information leading to their active use for marketing by the companies and organizations. Such marketing promotions are difficult to identify unlike the traditional medias like TV and newspaper. So, it is very much important to identify the promoters in the social media. Although, there are active ongoing researches, existing approaches are far from solving the problem. To identify such imposters, it is very much important to understand their strategies of social circle creation and dynamics of content posting. Are there any specific spammer types? How successful are each types? We analyze these questions in the light of social relationships in Twitter. Our analyses discover two types of spammers and their relationships with the dynamics of content posts. Our results discover novel dynamics of spamming which are intuitive and arguable. We propose ENWalk, a framework to detect the spammers by learning the feature representations of the users in the social media. We learn the feature representations using the random walks biased on the spam dynamics. Experimental results on large-scale twitter network and the corresponding tweets show the effectiveness of our approach that outperforms the existing approaches.


Social network Spam detection Feature learning 



This work is supported in part by NSF 1527364. We also thank anonymous reviewers for their helpful feedbacks.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • K. C. Santosh
    • 1
    Email author
  • Suman Kalyan Maity
    • 2
  • Arjun Mukherjee
    • 1
  1. 1.University of HoustonHoustonUSA
  2. 2.IIT KharagpurKharagpurIndia

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