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Public Sentiment under COVID-19

Introduction

The impact of the COVID-19 pandemic on public mental health has become increasingly prominent. Therefore, it is of great value to study the spatial-temporal characteristics of public sentiment responses to COVID-19 exposure to improve urban anti-pandemic decision-making and public health resilience. However, the majority of recent studies have focused on the macro scale or large cities, and there is a relative lack of adequate research on the small-city scale in China.

To address this lack of research, we conducted a case study of Shaoxing city, proposed a spatial-based pandemic-cognition-sentiment (PCS) conceptual model, and collected microblog check-in data and information on the spatial-temporal trajectory of cases before and after a wave of the COVID-19 pandemic. The natural language algorithm of dictionary-based sentiment analysis (DSA) was used to calculate public sentiment strength. Additionally, local Moran’s I, kernel-density analysis, Getis-Ord Gi* and standard deviation ellipse methods were applied to analyze the nonlinear evolution and clustering characteristics of public sentiment spatial-temporal patterns at the small-city scale concerning the pandemic.

The results reveal that (1) the characteristics of pandemic spread show contagion diffusion at the micro level and hierarchical diffusion at the macro level, (2) the pandemic has a depressive effect on public sentiment in the center of the outbreak, and (3) the pandemic has a nonlinear gradient negative impact on mood in the surrounding areas. These findings could help propose targeted pandemic prevention policies applying spatial intervention to improve residents’ mental health resilience in response to future pandemics.

Key words

spatial-temporal pattern evolutionpublic sentimentCOVID-19 pandemicmental health resiliencesocial media data

Conclusion

As there is a lack of fine research at the small-city scale, we proposed a spatial-based pandemic-cognition-sentiment (PCS) conceptual model and conducted a case study of Shaoxing city, China, at a small-city scale. Massive media data were collected to calculate the public sentiment. The methods of natural language processing (NLP), Getis-Ord Gi*, and standard deviation ellipse, etc., were used to measure the spatial-temporal characteristics of confirmed cases and the evolution pattern of public sentiment under the influence of COVID-19. Our findings verify that there is obvious agglomeration and differentiation in the spatial-temporal pattern of public sentiment, while the spatial-temporal evolution characteristics are closely related to the distribution characteristics of cases after the outbreak. Finally, targeted spatial strategies are proposed to improve people’s mental health resilience under pandemic conditions. The main conclusions are as follows:

(1) The spread of the pandemic is characterized by contagion and diffusion at the micro level and hierarchical diffusion at the macro level. According to the spatial characteristics of confirmed cases, the pandemic spread through droplet or contact transmission at the microscale, which reflects the characteristics of infection spread. At the macro scale, viruses spread in the direction of least resistance to spatial diffusion. With the help of the mobile network and its carriers, it preferentially propagates to the nodes with high centrality and high adjacency in the urban flow network, mainly showing the spatial characteristics of hierarchical diffusion.

(2) The pandemic has suppressed the emotional impact of residents in the center of the outbreak cluster. The sentiment value of the outbreak center increased significantly in the later stage of the outbreak, and this increase in sentiment positivity was mainly due to the stimulus of encouraging words. It is shown that with the development of the epidemic, public sentiment in the outbreak clusters gradually changed from low to proactive motivation and that there was a certain degree of emotional repression–incentive response.

(3) The pandemic has a negative gradient impact on public sentiment in the surrounding area. The COVID-19 pandemic had a significant negative impact on the emotions of residents in the areas surrounding the outbreak clusters. The intensity of the impact showed a gradient decline in terms of spatial proximity and accessibility, reflecting that the residents in the areas closer to the outbreak center were more significantly affected by the pandemic. Therefore, it can be concluded that the negative impact of the pandemic on public sentiment in surrounding areas with higher centrality and greater accessibility in the urban flow network is more obvious to a certain extent.

Paper Link: https://doi.org/10.3390/ijerph191811306 
Citation: Zhou Y., Xu J.*, et al.  Spatial-Temporal Pattern Evolution of Public Sentiment Responses to the COVID-19 Pandemic in Small Cities of China: A Case Study Based on Social Media Data Analysis. International Journal of Environmental Research and Public Health. 2022; 19(18):11306. https://doi.org/10.3390/ijerph191811306  ( SCI&SSCI  JCR Q1, IF:4.614 )

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