Content Trust Model for Detecting Web Spam

Abstract

As it gets easier to add information to the web via html pages, wikis, blogs, and other documents, it gets tougher to distinguish accurate or trustworthy information from inaccurate or untrustworthy information. Moreover, apart from inaccurate or untrustworthy information, we also need to anticipate web spam — where spammers publish false facts and scams to deliberately mislead users. Creating an effective spam detection method is a challenge. In this paper, we use the notion of content trust for spam detection, and regard it as a ranking problem. Evidence is utilized to define the feature of spam web pages, and machine learning techniques are employed to combine the evidence to create a highly efficient and reasonably-accurate spam detection algorithm. Experiments on real web data are carried out, which show the proposed method performs very well in practice.