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Predicting User Retweet Behaviors Based on Energy Optimization

Predicting microblog user retweet behaviors is the basis of building the information diffusion model in microblog social networks. In order to improve the accuracy of predicting user retweet behaviors, under the MRF (Markov Random Field) framework, the paper comprehensively analyzes the effects on user retweet behaviors caused by various features (e.g., user attributes and microblog contents) and the retweet behavior constraints between users, also constructs the corresponding energy function based on the logistic regression model to globally predict user retweet behaviors. Experimental results show the proposed method can accurately model user retweet behaviors and thus has high performance.