BDCC, Vol. 9, Pages 180: Modeling and Simulation of Public Opinion Evolution Based on the SIS-FJ Model with a Bidirectional Coupling Mechanism

Big Data and Cognitive Computing doi: 10.3390/bdcc9070180

Authors:
Wenxuan Fu
Renqi Zhu
Bo Li
Xin Lu
Xiang Lin

The evolution of public opinion on social media affects societal security and stability. To effectively control the societal impact of public opinion evolution, it is essential to study its underlying mechanisms. Public opinion evolution on social media primarily involves two processes: information dissemination and opinion interaction. However, existing studies overlook the bidirectional coupling relationship between these two processes, with limitations such as weak coupling and insufficient consideration of individual heterogeneity. To address this, we propose the SIS-FJ model with a bidirectional coupling mechanism, which combines the strengths of the SIS (Susceptible–Infected–Susceptible) model in information dissemination and the FJ (Friedkin–Johnsen) model in opinion interaction. Specifically, the SIS model is used to describe information dissemination, while the FJ model is used to describe opinion interaction. In the computation of infection and recovery rates of the SIS model, we introduce the opinion differences between individuals and their observable neighbors from the FJ model. In the computation of opinion values in the FJ model, we introduce the node states from the SIS model, thus achieving bidirectional coupling between the two models. Moreover, the model considers individual heterogeneity from multiple aspects, including infection rate, recovery rate, and individual susceptibility. Through simulation experiments, we investigate the effects of initial opinion distribution, individual susceptibility, and network structure on public opinion evolution. Interestingly, neither initial opinion distribution, individual susceptibility, nor network structure exerts a significant influence on the proportion of disseminating and non-disseminating individuals at termination. Furthermore, we optimize the model by adjusting the functions for infection and recovery rates.

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Wenxuan Fu www.mdpi.com