A Keyword Based Strategy for Spam Topic Discovery from the Internet

The increasing volume of spam has become a serious threat not only to the Internet, but also to the society. However, it's a great challenge to discover the spam from the Internet effectively and efficiently. Content-based filtering is one of the mainstream methods to solve the problem. This paper proposed a content based spam topic detection strategy through keyword extraction. In particular, spam topic is detected by using the topic model of multiple features with the keywords of clues, which integrate the corresponding feature of News, BBS and Blog. We get the min cost of 0.282 through TDT4 evaluating corpus and the satisfaction of 93.3% through the golaxy public opinion monitoring system of ICT, which is more effective than traditional method. The Experiments show that this algorithm is effective for spam topic detection.