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Detecting Spam Web Pages Through Content Analysis

Alexandros Ntoulas, Marc Najork, Mark Manasse, and Dennis Fetterly


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%).


Publication typeInproceedings
Published in15th International World Wide Web Conference (WWW)
AddressEdinburgh, Scotland
PublisherAssociation for Computing Machinery, Inc.
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