Yu Gao,1 Haiyan Liu,2 Yuechi Sun2 

1School of Psychology, Shandong Second Medical University, Weifang, Shandong, People’s Republic of China; 2School of Economics and Management, China University of Geosciences (Beijing), Beijing, People’s Republic of China

Correspondence: Haiyan Liu, School of Economics and Management, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian District, Beijing, 100083, People’s Republic of China, Email liuhy@cugb.edu.cn

Background: In the digital age, people’s attitudes and psychological security towards public health emergencies will be shared. Similar or identical psychological security states are prone to clustering and differentiation, while differentiated group psychological security is more prone to polarization, leading to group psychological security risks and then posing a threat to social stability and national security. However, existing studies mostly use qualitative analysis methods to study group emotional risks. There are still limitations in the study of dynamics of group psychological security risks through mining real data of social media.
Purpose: The study aims to use intelligent analysis methods to understand how group psychological security risks dynamically change.
Methods: The study draws on text sentiment analysis, Markov chains and time series analysis to construct a framework for the evolution of group psychological security risks. Based on this framework, text data was crawled on Sina Weibo platform, mainly consisting of posts during public health emergencies (March 1st to June 30th, 2022) in Shanghai, and a psychological security lexicon in the field of public health emergencies was constructed. This laid the foundation for identifying the tendencies, intensity, and transitions of individual text psychological security, and then exploring the evolution trend of group psychological security risks.
Results: Compared with the generation and reduction periods, group psychological security risks are more likely to occur during the outbreak and recovery periods, and the intensity level is also higher. The overall intensity of group psychological security risks shows an evolution trend of first increasing, then decreasing, and then increasing again.
Conclusion: The paper provides an opportunity to explore the dynamics of psychological security in the digital space. Meanwhile, we call on the government and relevant management departments to pay more attention to the group psychological security risks formed during the outbreak and recovery periods of public health emergencies, and take corresponding measures in a timely manner to guide the public to transform the extreme psychological security state into the normal psychological security state, in order to prevent and resolve group psychological security risks, promote social stability and national security.

Keywords: group psychological security risks, psychological security lexicon, machine learning, evolution trend