Risk Perception Visualization of Public Health Emergencies Based on Clustering Algorithms

Objective: To understand the progress, hotspots, trends and discrepancies of risk perception-related research in the context of public health emergencies, and to provide a reference for further research in the field. METHODS: This paper uses Citespace 6.2 R2 software based on clustering algorithm to search literature on risk perception of public health emergencies among university students in China Knowledge Network (CNKI) and Web of Science core collection database, and visualize and analyze authors, institutions and keywords. Results: In terms of the number of articles published, the proportion of research studies related to risk perception of public health emergencies increased year by year from 2020 to 2023, indicating that risk perception is receiving increasing attention. In terms of keyword clustering effects, both English and Chinese clusters have become tighter after the COVID-19 outbreak, indicating that much research has been conducted around risk perception after the COVID-19 outbreak. In terms of authors and institutions, the number of core authors posting articles is not high, the connection between authors is not strong, and the connection between institutions is low, which should strengthen the cooperation between authors and institutions. Conclusion: A comprehensive analysis shows that there is a growing interest in the study of risk perception in public health events, and although there are some differences in the issues of concern between China and overseas, the overall research trends are similar. Many studies have been conducted on risk perception and people's health, and they have become a hot topic for a while. More research is needed in the future on how to effectively intervene in the adverse effects of risk perception in public health emergencies.