A cascading diffusion prediction model in micro-blog based on multi-dimensional features

Abstract

Micro-blog, as a kind of weak relationship network, strengthens the communication among the bloggers, and propagates instant information in the social network. With the explosive growth of information flow in social network, researchers have a growing realization that it is essential to accurately predict the cascading diffusion of a message, which is of paramount importance to applications like public opinion monitoring, viral marketing and outbreaks detection. Although there have been extensive previous works on diffusion prediction, what kind of factors affects the information diffusion most and how to predict the propagation process are the focusing issues all the time. This paper analyzes the information dissemination and forwarding mechanism in the social network. In particular, we extract main features from multiple dimensions including node attributes, message content characteristics and the topology relation between nodes. Based on these features, this paper proposed a cascades diffusion model to predict the propagation process. Besides, we quantitatively evaluated the weights of the features in the proposed model by a stochastic gradient descent algorithm. We evaluate the proposed method on Sina micro-blog dataset. The experimental results show that the proposed method outperforms the other common models in precise prediction.