Dynamic Evolution of Public’s Positive Emotions and Risk Perception for the COVID-19 Pandemic: A Case Study of Hubei Province of China

Abstract

The spread of COVID-19 pandemic and the participation of Internet information are continually changing the public’s positive emotions and risk perception. However, relatively little is known about the underlying mechanism of how the COVID-19 dynamic situation affects the public’s risk perception and emotions. This study uses the social risk amplification framework (SRAF) as the theoretical basis to collect and analyze Hubei Province data from January 20 to April 8, 2020, including the number of newly diagnosed people per day, the proportion of positive emotional posts in Weibo, and the Baidu search index (BSI). The autoregressive integrated moving average (ARIMAbased time-series prediction model is used to analyze the dynamic evolution laws and fluctuation trends of Weibo positive emotions and risk perception during the development of the pandemic. The conclusion of the study is that positive emotions are negatively correlated with risk perception, the severity of the pandemic situation is negatively correlated with positive emotions, and the severity of the pandemic situation is positively correlated with risk perception. The public has a keen response to the dynamics of the pandemic situation and the government’s decision-making behavior, which is manifested by the significant changes in positive emotions and risk perception in the corresponding period. The research results can provide a reference for government departments to guide the public to establish an objective risk perception and maintain positive and stable emotions in similar catastrophes.

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