http://www.bibsonomy.org/burst/user/beate/featuresBibSonomy BuRST Feed for /user/beate/features2008-09-06T20:51:32+02:00Detecting spam web pages through content analysishttp://www.bibsonomy.org/bibtex/2c93f4228fd8552bede071569cdaa1ad9/beatebeate2008-04-07T10:41:46+02:00spam features web <span style="color:#555555;">Alexandros <a href="http://www.bibsonomy.org/author/Ntoulas">Ntoulas</a> und Marc <a href="http://www.bibsonomy.org/author/Najork">Najork</a> und Mark <a href="http://www.bibsonomy.org/author/Manasse">Manasse</a> und Dennis <a href="http://www.bibsonomy.org/author/Fetterly">Fetterly</a> </span><em>WWW '06: Proceedings of the 15th international conference on World Wide Web, </em><em>Seite83--92. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Mon Apr 07 10:41:46 CEST 2008New York, NY, USAWWW '06: Proceedings of the 15th international conference on World Wide Web83--92Detecting spam web pages through content analysis2006spam features web 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%).