GDTM: A Gaussian Dynamic Topic Model for Forwarding Prediction Under Complex Mechanisms

Abstract

Forwarding is the keyway for information propagation in social networks that affected by complex factors. Due to the fact that the forwarding mechanisms are essentially unclear, this paper focuses on the formation and evolution of the external as well as internal driving mechanisms and develops a Gaussian dynamic topic model for forwarding prediction by incorporating all the information of nodes and edges. First, based on the diversity of communities each user located in, latent Dirichlet allocation (LDA) traditional text modeling method is applied to user following relationships modeling that leads to the initial formation of communities, and user interacting relationships modeling that leads to the evolution of communities. Taking the advantage of LDA topic model in dealing with the problem of polysemy and synonym, we can mine user latent distribution over communities and analyze the external community driving effects. Second, considering the differences in individual habits, time factor is introduced and the dynamic topic model is proposed to model user behavioral attributes. Meanwhile, in regard to continuous user attributes modeling, the parameter of topic-word distribution in the topic model is replaced by multivariate Gaussian distributions, which shown to be effective at capturing the regularities of individual behavioral habits and analyzing the internal individual driving effects. Finally, combining with external and internal factors, a probabilistic graph model is used to modeling forwarding behavior. We propose methods based on Gibbs sampling and an expectation–maximization algorithm to estimate model parameters and fit our model to predict user forwarding actions. Experimental results indicate that the model can not only detect the latent communities but also can improve the performance of forwarding prediction effectively.