Comments for a product or a news article are rapidly growing and became a medium of measuring quality products or services. Consequently, spammers have been emerged in this area to bias them toward their favor. In this paper, we propose an efficient spammer detection method using structural rank of author specific term-document matrices. The use of structural rank was found effective and far faster than similar methods.
In recent years, social Web sites have become important components of the Web. With their success, however, has come a growing influx of spam. If left unchecked, spam threatens to undermine resource sharing, interactivity, and openness. This article surveys three categories of potential countermeasures - those based on detection, demotion, and prevention. Although many of these countermeasures have been proposed before for email and Web spam, the authors find that their applicability to social Web sites differs.