Weibo core user mining and propagation scale predicting

Abstract

The research of information dissemination on Weibo(a twitter-like service in China) can help us to better understand the trend of network propagation, and the study of core users on the Internet is of great significance to the control of network public opinion. First, this paper identifies the core users of each tweet by classifying different Weibo events and analyzing its information fragments, information coupling, and propagation hierarchy based on the content and propagation relationship of Weibo news. Furthermore, a new Weibo network information dissemination model was constructed based on the propagation relationship of Weibo users and the classical infectious disease model. Then, according to the propagation pattern of Weibo news, the propagation scale of a single tweet is predicted, and this paper studied the mechanism and effects (impulse effect, clock effect, and herding effect) and the scale of propagation of Internet users in online information dissemination. Finally, the authors conducted simulations in a scale-free network and, meanwhile, compared the results with real Weibo propagation data. The results show that the propagation model proposed in this paper is not only reliable and effective, but also can predict the propagation scale of information on Weibo.