Evolution and governance of online public opinion during COVID-19: a hybrid approach using communication visualization, SIR modeling, and simulation validation

Abstract

IntroductionThis study investigates the mechanisms of public opinion dissemination and governance strategies during public health events, using a two-stage SIR model informed by the Information Cascade Theory.MethodsThe research employs Gephi visual analysis to identify principal nodes of public opinion and combines model simulations with dynamic propagation analysis to verify the model's precision and applicability.ResultsThe findings reveal that pivotal information nodes significantly accelerate the spread of public opinion, while ordinary nodes contribute to the natural attenuation of public discourse due to their strong spontaneous recovery capabilities. The simulation analysis further identifies the optimal timing for government intervention, particularly during the initial and peak phases of public opinion dissemination.DiscussionBased on the results, the study recommends strategies to strengthen the management of key opinion nodes, enhance public information literacy, optimize policy implementation, and utilize simulation tools to assist in public opinion management. These recommendations offer valuable theoretical and practical insights for managing public opinion during public health events.