Two-layer coupled network model for topic derivation in public opinion propagation

In view of the fact that news can generate derivative topics when it spreads through micro-blogs, a two-layer coupled SEIR public opinion propagation model is proposed in this paper. The model divides the process of public opinion propagation into two layers: the original topic layer and the derived topic layer. Messages are transmitted separately by the SEIR model in the two topic layers, which are independent and interactive. The influence of the topic derivation rate on the propagation trend is established by solving for the equilibrium point and propagation threshold. Further, we establish the relationship between the original topic and the derived topic by simulation. This paper uses the Baidu index to demonstrate the correctness of the model. The relationship between the derived topic and the original topic is verified by adjusting the parameters by the control variable method. The results show that the proposed model is consistent with the propagation of actual public opinion.