Detecting Spam Web Pages through Content Analysis

by: Alexandros Ntoulas, Marc Najork, Mark Manasse, Dennis Fetterly

(May 2006), pp. 83-92.

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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%).

@inproceedings{ntoulas06detecting, 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\%).}, address = {Edinburgh, Scotland}, author = {Ntoulas, Alexandros and Najork, Marc and Manasse, Mark and Fetterly, Dennis }, booktitle = {Proceedings of the World Wide Web conference}, citeulike-article-id = {489538}, doi = {http://dx.doi.org/10.1145/1135777.1135794}, keywords = {adversarial-ir}, month = {May}, pages = {83--92}, posted-at = {2006-02-02 13:48:00}, priority = {0}, title = {Detecting Spam Web Pages through Content Analysis}, url = {http://dx.doi.org/10.1145/1135777.1135794}, year = {2006} }

RIS record

TY - CONF ID - ntoulas06detecting L3 - citeulike-article-id:489538 TI - Detecting Spam Web Pages through Content Analysis T2 - Proceedings of the World Wide Web conference SP - 83 EP - 92 AD - Edinburgh, Scotland N2 - 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%). KW - adversarial-ir AU - Ntoulas, Alexandros AU - Najork, Marc AU - Manasse, Mark AU - Fetterly, Dennis PY - 2006/05// UR - http://dx.doi.org/10.1145/1135777.1135794 ER -

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