A rumor spreading model based on user browsing behavior analysis in microblog

Microblog has already been one of the most popular communication channels on the Internet, and also has become the third part of the public opinion source in China. This paper describes the behavior of browsing messages in microblog to calculate the probability of a message to be browsed after releasing and proposed an epidemic model to explain how a rumor gets spread among followers with the influence of different rumor refuting measures. We investigate the evolution of retweeting behavior by gathering data through Sina API. By establishing a human dynamics model for deducing retweeting behavior, the result reveals that the distribution of the probability of a message to be browsed over time presents power-law characters. This indicates that the messages are time-bound, which offers suggestions for message releasing of Micro-blogging Marketing. As for the propagation of rumor, we chose a specific rumor and firstly process its retweet count under the consideration of microblog users' active periods based on empirical analysis. Then, an improved rumor spreading SIR model, which takes the impact of rumor refuting by the affected enterprise, a microblogging opinion leader and microblogging platform into account, is constructed. According to the simulation results, this model shows how these rumor refuting measures influence the propagation of rumor, reflecting the most effective measure to shrink the transmission of rumor via microblog community.