Spam web page detection using combined content and link features Online publication date: Mon, 12-Sep-2016
by Rajendra Kumar Roul; Shubham Rohan Asthana; Gaurav Kumar
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 8, No. 3, 2016
Abstract: Web spamming refers to actions that have intentions to mislead search engines by ranking some irrelevant web pages higher in the search results than they deserve. It is thus a roadblock in obtaining high-quality information retrieval from the web. Spam web pages are often littered with irrelevant and meaningless content. Therefore, spam detection methods have been proposed as a solution for web spam in order to minimise the adverse effects of spam web pages. There has been no single defining profile that can encompass all types of spam websites. As such, this makes spam web page detection extremely difficult. In this paper, the proposed technique combines the content and link-based features of web pages to classify them as spam or non-spam. For experimental purpose, WEBSPAM-UK2006 dataset has been used. The results of the proposed approach were compared with the existing approaches and it has been found that the F-measure of the proposed approach outperformed the others.
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