document: Detecting spam web pages through content analysis 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 % ) .
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