Identifying Link Farm Spam Pages

by: Baoning Wu, Brian D Davison

(May 2005)

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Abstract

With the increasing importance of search in guiding today's web traffic, more and more effort has been spent to create search engine spam. Since link analysis is one of the m ost important factors in current commercial search engines' ranking systems, new kinds of spam aiming at links have appeared. Building link farms is one technique that can de teriorate link-based ranking algorithms. In this paper, we present algorithms for detecting these link farms automatically by first generating a seed set based on the common link set between incoming and outgoing links of Web pages and then expanding it. Links between identified pages are reweighted, providing a modified web graph to use in ranki ng page importance. Experimental results show that we can identify most link farm spam pages and the final ranking results are improved for almost all tested queries.

@inproceedings{citeulike:336502, abstract = {With the increasing importance of search in guiding today's web traffic, more and more effort has been spent to create search engine spam. Since link analysis is one of the m ost important factors in current commercial search engines' ranking systems, new kinds of spam aiming at links have appeared. Building link farms is one technique that can de teriorate link-based ranking algorithms. In this paper, we present algorithms for detecting these link farms automatically by first generating a seed set based on the common link set between incoming and outgoing links of Web pages and then expanding it. Links between identified pages are reweighted, providing a modified web graph to use in ranki ng page importance. Experimental results show that we can identify most link farm spam pages and the final ranking results are improved for almost all tested queries.}, author = {Wu, Baoning and Davison, Brian D. }, booktitle = {Proceedings of the 14th International World Wide Web Conference 2005}, citeulike-article-id = {336502}, keywords = {link-spam, web-graph}, month = {May}, posted-at = {2005-09-30 11:47:31}, priority = {0}, title = {Identifying Link Farm Spam Pages}, year = {2005} }

RIS record

TY - CONF ID - citeulike:336502 L3 - citeulike-article-id:336502 N2 - With the increasing importance of search in guiding today's web traffic, more and more effort has been spent to create search engine spam. Since link analysis is one of the m ost important factors in current commercial search engines' ranking systems, new kinds of spam aiming at links have appeared. Building link farms is one technique that can de teriorate link-based ranking algorithms. In this paper, we present algorithms for detecting these link farms automatically by first generating a seed set based on the common link set between incoming and outgoing links of Web pages and then expanding it. Links between identified pages are reweighted, providing a modified web graph to use in ranki ng page importance. Experimental results show that we can identify most link farm spam pages and the final ranking results are improved for almost all tested queries. TI - Identifying Link Farm Spam Pages T2 - Proceedings of the 14th International World Wide Web Conference 2005 KW - link-spam KW - web-graph AU - Wu, Baoning AU - Davison, Brian PY - 2005/05// ER -

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