SDHM: A hybrid model for spammer detection in Weibo
Yu Liu, Bin Wu, Bai Wang, Guanchen Li · 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014) · 2014
As the microblogging service (such as Weibo) is becoming popular, spam becomes a serious problem of affecting the credibility and readability of Online Social Networks. Most existing studies took use of a set of features to identify spam, but without the consideration of the overlap and dependency among different features. In this study, we investigate the problem of spam detection by analyzing real spam dataset collections of Weibo and propose a novel hybrid model of spammer detection, called SDHM, which utilizing significant features, i.e. user behavior information, online social network attributes and text content characteristics, in an organic way. Experiments on real Weibo dataset demonstrate the power of the proposed hybrid model and the promising performance.