Deep Recognition of Public Opinion Reversals in Critical Incidents Based on Hypernetwork Architecture

Abstract

With the development of social media and online platforms, the speed of dissemination and influence of emergencies in cyberspace have increased significantly. The rapid change of public opinion, especially the reversal of public opinion, may have a significant impact on social stability and government credibility. The hypernetwork structure has complex multilevel and multidimensional characteristics, and it is of great significance to analyze the multiple participating subjects of public opinion evolution and their complex relationships based on the hypernetwork theory, and to further identify the public opinion reversal for the public opinion response and guidance of emergencies. According to the complex interaction between the participants of emergencies and internal and external factors, this paper constructs a hypernetwork model including four subnets of users, time series, opinions, and emotions, and analyzes the network structure in detail. On this basis, the method steps of emergency public opinion inversion recognition are proposed. Taking the public opinion event caused by Hongxing Erke donation during the rainstorm in Henan Province of China as an example, the empirical analysis is carried out. The research shows that the proposed emergency hypernetwork model provides effective support for the identification of public opinion inversion, and the identification method of public opinion inversion based on the hypernetwork is helpful to find the trend of public opinion evolution, so as to infer the tendency of public opinion inversion, which provides new ideas for the related research of public opinion monitoring and emergency response.