Abstract: As an outcome of Web 2.0 era and a rising social media, microblog service has been playing a more and more important role in people’s daily life. It serves as not only a bridge of communication and information sharing, but also a crucial way to acquire information.As a mixture of social network and information media, microblog has a diverse ecological environment.We media accounts as a component of microblog, have been taking rapid development.In this paper, we creatively introduce the we media account detection problem and illustrate its meaning, then we propose a comprehensive feature set from account profile, posting behavior and posting content.Based on these features, we perform a supervised learning method to detect we media account. Experimental results show that: 1) we media accounts distinct from general accounts in the environment of Sina Weibo, and the difference is mainly on the behavior of publishing microblogs and the topic of microblogs. 2) The proposed three feature sets are effective for we media account detection, and they complement with each other as well, achieving an impressively high accuracy of 96.71%.

Key words: microblog, we media account, classification, support vector machine(SVM), supervised learning