Online Social Information Propagation Analysis Based on Time-Delay Mixture Diffusion Model

Information propagation modeling is an important problem in social media analysis. A lot of existing methods assume that information propagation process follows a uniform pattern. Different from the previous studies, we analyze the factors affecting the information transmission probabilities between users from the aspects of users' interest and community-level structure features, and propose a time-delay mixture diffusion model to describe the dynamics of information propagation process in online social networks (OSNs). In this work, we model the information propagation based on mixture of patterns by considering both content and two levels of structural features. We conduct the experiments on the Sina-Weibo dataset, one of the largest microblogging websites in China. The experimental results show that our proposed model outperforms the baseline models in information diffusion prediction.