The Coronavirus Disease (COVID-19) is an urgent global public health crisis. During the outbreak, China actively organized anti-epidemic activities, and gradually achieved the staged victory of epidemic prevention and control. It is of practical significance to objectively understand the public opinion response and regional differences for improving policy control and scientific governance during major public health events.
In this study, originating in microblogs data, a topic extraction and classification model is constructed based on the Latent Dirichlet Allocation (LDA) topic model and the random forest algorithm. The hierarchical processing method from total to sub is adopted to identify 7 primary and 12 secondary theme topics about public opinion in microblog. The general distribution of public opinion is analyzed in terms of amount, space, time sequence, and content from January 9 to March 10, 2020, and its regional distribution characteristics were explored in key regions like Hubei Province, four major urban agglomerations and border ports.
The results show that the change of microblogs counts on each topic is positively related to the evolution of the COVID-19 epidemic, and the temporal and spatial distribution of public opinion is related to the severity of the epidemic, the degree of population aggregation, and the level of economic development. The spatial distribution of all topics is significantly consistent, but the spatial distribution within these areas is obviously different. The response of Chinese people is rational and positive. It is also found that the government response lags behind the social media, the imbalance of resource allocation caused by the sharp rise of relief information in the short term is prominent, and the difference in response policies of various urban agglomeration areas combined with its own regional characteristics are not obvious. It is suggested to continue to strengthen the focus of public opinion on epidemic situation in key areas and the differentiated and accurate response to local conditions.
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Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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