EviRank: An Evidence Based Content Trust Model for Web Spam Detection

  • Wei Wang
  • Guosun Zeng
  • Mingjun Sun
  • Huanan Gu
  • Quan Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4537)


Creating an effective spam detection method is a challenging task. Traditional works usually regard this kind of work as a problem of binary classification. In this paper, however, we argue that it is more property to use the notion of content trust for it, and regard it as a ranking or ordinal regression problem. Evidence is utilized to define the feature of spam web pages, and machine learning techniques are employed to combine the evidence to create a highly efficient and reasonably-accurate detection algorithm. Experiments on real web data are carried out, which improve the proposed method performs very well in practice.


web spam evidence content trust ranking SVM learning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wei Wang
    • 1
  • Guosun Zeng
    • 1
  • Mingjun Sun
    • 1
  • Huanan Gu
    • 1
  • Quan Zhang
    • 1
  1. 1.Department of Computer Science and Technology, Tongji University, Shanghai 201804, China, Tongji Branch, National Engineering & Technology Center of, High Performance Computer, Shanghai 201804, China, The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Email: 

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