Prediction of information diffusion in social networks using dynamic carrying capacity

Abstract

Online social networks have become an effective channel for influencing millions of users by facilitating exchange and spread of information. Despite recent works on modeling information diffusion in social networks, the complexity of social interactions makes quantification of any spreading phenomenon in social networks a challenging task. Most of the research in this area rely on empirical or statistical approaches without considering the temporal aspects and the carrying capacity of the networks. In this paper, we capture the temporal evolution of information spread in a social network using linear ordinary differential equations (ODEs). Our proposed model shows the influence of users and their temporal actions on the carrying capacity. We validate the diffusion process across the network using a dataset collected from Digg which is a popular social news sharing website. The results show that our dynamic carrying capacity PDE model is able to predict with high accuracy how the information diffuses in the network during the different phases of the lifetime of a news story. We also propose a model to represent the carrying capacity based on the portion of the influenced users.