Analysis of Rumor Spreading in Communities Based on Modified SIR Model in Microblog

Abstract

Rumor spreading as a basic mechanism for information on online social network has a significant impact on people’s life. In Web 2.0 media age, microblog has become a popular means for people to gain new information. Rumor as false information inevitably become a part of this new media. In this study, a modified rumor spreading model called SIRe is introduced, which compared to traditional rumor spreading model, have included the stifler’s broadcasting effect and social intimacy degree between people. In order to verify the reasonableness of SIRe model, real rumor spreading data set and microblog network structure data set are obtained using Sina API. Then rumor predicting results using different models are compared. Finally, for the purpose of finding the characteristics of rumor spreading in community scale, a clustering method is used to discover the user communities. Analysis results have revealed that communities with higher closeness centrality tend to have higher max ratio of spreaders, and scattered immunization is better than centralized immunization, resulting lower max ratio of spreaders. Both the results and explanations are shown in this paper.