Discovering Massive High-Value Users from Sina Weibo Based on Quality and Activity

This paper proposes a method to discover the high-value users who are both high-quality and high-activity. First, a Trust Transfer Model is introduced to capture users with high-quality in Sina Weibo that is a social web in China. Then, analysis and discussion are shown to identify the users are high-quality. Next, fresh users who are busy reposting are captured into the dataset filled with the high-quality users. Considering that reposting stands for the high-activity, hence the four degrees including reposting are proposed to judge one user\´s quality and activity. We discuss the effects of the four degrees. And then, the method called "WeiboRank" based on the four degrees is proposed to mine the high-value users. Finally, testing for the degree of coverage based on the "Top10 Hot Microblog" in Weibo presents a relatively high credibility of our dataset. The evaluation indicates that the users mined in this paper are quite high-quality and high-value. In conclusion, we will continue efforts to discover the high-value users in microblog and believe that discovering and maintaining a dataset filled with the high-value users which is not too big is quite significant for both academic research and business applications.