Detecting spam web pages using content and link-based techniques

Abstract

Web spam is a technique through which the irrelevant pages get higher rank than relevant pages in the search engine’s results. Spam pages are generally insufficient and inappropriate results for user. Many researchers are working in this area to detect the spam pages. However, there is no universal efficient technique developed so far which can detect all spam pages. This paper is an effort in that direction, where we propose a combined approach of content and link-based techniques to identify the spam pages. The content-based approach uses term density and Part of Speech (POS) ratio test and in the link-based approach, we explore the collaborative detection using personalized page ranking to classify the Web page as spam or non-spam. For experimental purpose, WEBSPAM-UK2006 dataset has been used. The results have been compared with some of the existing approaches. A good and promising F-measure of 75.2% demonstrates the applicability and efficiency of our approach.