How the anti-rumor kills the rumor: Conflicting information propagation in networks

Online Social Networks (OSNs) is taking over television and newspapers, to be the dominant information dissemination option. The growing involvement of individuals create the situation that colliding, even contradicting information coexist and propagate in the same network, which gives rise to an interesting question: how will the conflicting information propagate? To answer this question, the propagation process is described to be an Susceptible-Infected-Cured (SIC) epidemic, and we propose an inference algorithm to study the transient behavior of the competing propagation processes in connected networks. Moreover, we provide an analytic method to derive the conditional infection count distribution for networks with special topologies, as a step further to understand the evolution. A trace collected from the Internet is analyzed to validate our model and methods.