Dynamical Behavior of Rumor in Online Social Networks

Abstract

As a powerful tool for phase transition analysis, the mean-field theory of rumor spreading brings us a new method for research in Online Social Networks (OSN). Based on the theory of complex networks, we simulate the complex process of rumor spreading in OSN. Using numerical simulations with focus on the information spreading threshold as well as critical behaviors, we study the Susceptible-Infected-RecoveredSusceptible (SIRS) information dynamics model in OSN with asymmetric propagation. Basic principle and structure of OSN were analyzed. We present a rumor spreading model, where the influence of the neighbor trees is treated in a more realistic way and the definition of a neighborhood can be tuned by an additional parameter. Based on the mean-field theory approach, a contact network model with scale-free property is built. It is demonstrated that the asymmetry of propagation plays important role: we could redistribute the asymmetry to balance the degree heterogeneity of the network and then to restore the information threshold to a finite value. The relationship of spreading probability, network size, infected fraction and infected ratio are showed respectively in the paper. Our model exhibits a surprisingly sharp phase transition which can be shifted by a redefinition of the neighborhood. The result shows that the SIRS model built in this paper is valid and the simulation of the information propagation is feasible.