Precise Modeling Rumor Propagation and Control Strategy on Social Networks

Abstract

In this paper, we first point out the limitations of the classic epidemic spreading model SIR in representing rumor spreading processes, and then identify the effect ofpublic opinions in dividing Infected states (I) into Positive infected (P) states and Negative infected (N) states. Based on this observation, we propose a newSPNR model. To evaluate the proposed model, we compare the simulation results with the real-world data obtained on Sina Weibo, the largest micro-blogging tool in China. The results showthat the newmodel effectively captures the rumor spreading process. Furthermore, to develop an effective rumor control strategy, we propose an opinion guidance rumor control strategy based onSPNRmodel.We compare different control strategies using real-world data sets and a synthetic network generated according to a scale-free model. The results show that the proposed strategy is effective in fighting rumor spreading.