Web spam detection using SVM classifier

Web spam is one of the recent problems of search engines because it powerfully reduced the quality of the Web page. Web spam has an economic impact because spammers provide a large free advertising data or sites on the search engines and so an increase in the web traffic. In this paper we have implemented spam detection system based on a SVM classifier that combines new link features with content and qualified link analysis. We have used the kullback-Leibler divergence for characterizing the relationship between the two linked pages. The experimental result shows the F-measure 0.95% for WEBSPAM-UK2006 and 0.44% for WEBSPAM-UK2007 datasets.