How Does Public Opinion Evolve in the Post-Truth Era? A Modeling and Simulation Study of Group Negative Emotion in Deviation Communities

In the post-truth era, effective governance of emergency public sentiment faces significant challenges due to the phenomenon of opinion deviation. Although online public opinion has been extensively investigated, the specific impact of Public Opinion Deviation (POD) on the evolution of group negative emotion remains inadequately understood. To address this gap, this study proposes an explicable framework that integrates community detection, text mining, and opinion dynamics. Opinion deviation communities are identified by applying the Louvain algorithm and TextRank to social media data, followed by a deviation analysis of community topics against core issues. Subsequently, a multi-stage quantification model is constructed to measure the severity of POD. During these processes, we develop a novel Opinion Dynamics F-J model (POD F-J model) and its intervention-oriented model (IPOD F-J model), which incorporate the quantified POD severity to simulate the evolution of group negative emotion. Our findings demonstrate an intrinsic correlation between the severity of opinion deviation and the intensity of group negative emotion at the information level, thereby confirming the necessity of targeted intervention. Simulation experiments indicate that different intervention strategies should be adopted depending on the situation. Moreover, the application of a greedy algorithm identifies the time points corresponding to the peak severity of deviation and its onset as the efficiency-oriented intervention timings. This study provides a data-driven framework for monitoring and mitigating emotional contagion in deviation communities, contributing to both the theory and practice of digital governance.