Fractional-order SS1IR Model for Predicting Public Opinion Dissemination in Social Networks

With over one billion Internet users in China, a large amount of public opinion is spreading rapidly in breadth and depth at an unprecedented speed in social networks. The es-tablishment of an accurate public opinion dissemination (POD) model is vital for predicting and maintaining the construction of online civilization. On the basis of the traditional SIR model, a fractional-order differential is combined to solve the problem of "anomalous dissemination" in the process of actual POD. The influence of web hypers on POD in social networks is also discovered, and the S1 super-spreading node is introduced. In this paper, we propose a fractional-order SS1IR POD prediction model. The experimental results show that the model fits real data well and has small errors. Therefore, our model can play an aggressive and significant role for predicting POD in social networks.