Detecting Web Spams Using Evidence Theory

Moitrayee Chatterjee, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Search engines are the major instruments on the Web. The determination of the liability of the results returned by a typical search engine is a daunting challenge mainly due to the presence of Web spams. New types of Web spams are continuously introduced every now and then, which makes it drastically challenging to decide about the accuracy of the results. The problem looks like a reasoning problem in the presence of uncertainty. This paper presents a methodology for predicting Web spam where the spamicity of hosts is formulated as a reasoning problem. The approach is based on evidence theory, a mathematical prediction model based on Dempster-Shafer Theory (DST). The key benefit of our approach for Web spam is DST's ability to deal with the uncertainty. When a new spam is introduced in the system, the system lacks a reasonable prior knowledge. This is where DST provides more liable solution to detect spams without any prior information. The paper presents detailed statistical evaluations of the proposed approach where an accuracy of 99.27% in detecting Web spams is reported.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018
EditorsClaudio Demartini, Sorel Reisman, Ling Liu, Edmundo Tovar, Hiroki Takakura, Ji-Jiang Yang, Chung-Horng Lung, Sheikh Iqbal Ahamed, Kamrul Hasan, Thomas Conte, Motonori Nakamura, Zhiyong Zhang, Toyokazu Akiyama, William Claycomb, Stelvio Cimato
PublisherIEEE Computer Society
Pages695-700
Number of pages6
ISBN (Electronic)9781538626665
DOIs
StatePublished - Jun 8 2018
Event42nd IEEE Computer Software and Applications Conference, COMPSAC 2018 - Tokyo, Japan
Duration: Jul 23 2018Jul 27 2018

Publication series

NameProceedings - International Computer Software and Applications Conference
Volume2
ISSN (Print)0730-3157

Conference

Conference42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
CountryJapan
CityTokyo
Period07/23/1807/27/18

Keywords

  • Basic Probability Assignment
  • Belief
  • Dempster-Shafer Theory
  • Plausibility
  • Web Spam

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  • Cite this

    Chatterjee, M., & Namin, A. S. (2018). Detecting Web Spams Using Evidence Theory. In C. Demartini, S. Reisman, L. Liu, E. Tovar, H. Takakura, J-J. Yang, C-H. Lung, S. I. Ahamed, K. Hasan, T. Conte, M. Nakamura, Z. Zhang, T. Akiyama, W. Claycomb, & S. Cimato (Eds.), Proceedings - 2018 IEEE 42nd Annual Computer Software and Applications Conference, COMPSAC 2018 (pp. 695-700). [8377949] (Proceedings - International Computer Software and Applications Conference; Vol. 2). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2018.10321