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Return to Session 1: Web Querying and Mining
The increasing importance of search engines to commercial web sites has given rise to a phenomenon we call "web spam", that is, web pages that exist only to mislead search engines into (mis)leading users to certain web sites. Web spam is a nuisance to users as well as search engines: users have a harder time finding the information they need, and search engines have to cope with an inflated corpus, which in turn causes their cost per query to increase. Therefore, search engines have a strong incentive to weed out spam web pages from their index. We propose that some spam web pages can be identified through statistical analysis: Certain classes of spam pages, in particular those that are machine-generated, diverge in some of their properties from the properties of web pages at large. We have examined a variety of such properties, including linkage structure, page content, and page evolution, and have found that outliers in the statistical distribution of these properties are highly likely to be caused by web spam. This paper describes the properties we have examined, gives the statistical distributions we have observed, and shows which kinds of outliers are highly correlated with web spam.
@inproceedings {DBLP:conf/webdb/FetterlyMN04, author = {Dennis Fetterly and Mark Manasse and Marc Najork}, title = {Spam, Damn Spam, and Statistics: Using Statistical Analysis to Locate Spam Web Pages}, pages = {1-6}, ee = {http://webdb2004.cs.columbia.edu/papers/1-1.pdf}, booktitle = {Seventh International Workshop on the Web and Databases}, month = Jun, year = 2004, crossref = {conf/webdb/2004} }  ©2005 Association for Computing Machinery |
