Automatically generated spam detection based on sentence-level topic information
Yoshihiko Suhara, Hiroyuki Toda, Shuichi Nishioka, Seiji Susaki · 2013
Spammers use a wide range of content generation techniques with low quality pages known as content spam to achieve their goals. We argue that content spam must be tackled using a wide range of content quality features. In this paper, we propose novel sentence-level diversity features based on the probabilistic topic model. We combine them with other content features to build a content spam classifier. Our experiments show that our method outperforms the conventional methods.