Information diffusion through online social networks

The rapid development of online social networks has attracted much attention all over the world. Communications through online communities are becoming prevalent among people of all ages. Though online social networks provide us a platform to share opinions publicly, this mechanism will also lead to the generation of rumors especially in case of emergency. So it is urgent for researchers to figure out the information diffusion dynamics through this new kind of media to prevent rumors. Information diffusion mechanisms have been widely studied since the advent of complex network theory and infectious disease model. Most models and simulations are based on classical network structures. In this paper, we explore the information diffusion mechanism by using an online social network dataset from Facebook. First, we use SIS model to simulate the diffusion process of one specific piece of news, further we develop a new mechanism to model a more realistic situation with growing topics and decreasing infectiousness. By comparing simulation results of information diffusion dynamics on different network structures, we discover that people in online social networks are more likely to be infected due to mass connections and information overload.