Detecting spam web pages through content analysis

  • Authors:
  • Alexandros Ntoulas;Marc Najork;Mark Manasse;Dennis Fetterly

  • Affiliations:
  • UCLA Computer Science Dept., Los Angeles, CA;Microsoft Research, Mountain View, CA;Microsoft Research, Mountain View, CA;Microsoft Research, Mountain View, CA

  • Venue:
  • Proceedings of the 15th international conference on World Wide Web
  • Year:
  • 2006

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Abstract

In this paper, we continue our investigations of "web spam": the injection of artificially-created pages into the web in order to influence the results from search engines, to drive traffic to certain pages for fun or profit. This paper considers some previously-undescribed techniques for automatically detecting spam pages, examines the effectiveness of these techniques in isolation and when aggregated using classification algorithms. When combined, our heuristics correctly identify 2,037 (86.2%) of the 2,364 spam pages (13.8%) in our judged collection of 17,168 pages, while misidentifying 526 spam and non-spam pages (3.1%).