Predicting the Popularity of News Based on Competitive Matrix

With the rapid development of network, more and more people share and comment on the web to express their mends. How to predict the popularity of topic happening recently is a hot topic and lots of people are trying to find out the law of information diffusion hidden in it. However, many models assume that information spreads with no external interference in social networks. The research on competitive diffusion is still at the primary stage. The main contribution is to solve the problem that there are few or no work for popularity prediction based on multi-information, and propose a predicting model based on competitive matrix. The goal of this paper is to accurately estimate the popularity for a given viral topic at final based on the observation of historical popularity of the topic. And this model is mainly based on the competitive matrix and gradient descent method. Also, the capability of this method provides a better performance in the popularity prediction according to an empirical study on Tencent News.