Kahanek Alexander, Yu Xinchen, Hong Lingzi, Cleveland Ana, Philbrick Jodi
College of Information University of North Texas Denton, TX United States.
JMIR Infodemiology. 2021 Dec 30;1(1):e31671. doi: 10.2196/31671. eCollection 2021 Jan-Dec.
During the COVID-19 pandemic, US public health authorities and county, state, and federal governments recommended or ordered certain preventative practices, such as wearing masks, to reduce the spread of the disease. However, individuals had divergent reactions to these preventive practices.
The purpose of this study was to understand the variations in public sentiment toward COVID-19 and the recommended or ordered preventive practices from the temporal and spatial perspectives, as well as how the variations in public sentiment are related to geographical and socioeconomic factors.
The authors leveraged machine learning methods to investigate public sentiment polarity in COVID-19-related tweets from January 21, 2020 to June 12, 2020. The study measured the temporal variations and spatial disparities in public sentiment toward both general COVID-19 topics and preventive practices in the United States.
In the temporal analysis, we found a 4-stage pattern from high negative sentiment in the initial stage to decreasing and low negative sentiment in the second and third stages, to the rebound and increase in negative sentiment in the last stage. We also identified that public sentiment to preventive practices was significantly different in urban and rural areas, while poverty rate and unemployment rate were positively associated with negative sentiment to COVID-19 issues.
The differences between public sentiment toward COVID-19 and the preventive practices imply that actions need to be taken to manage the initial and rebound stages in future pandemics. The urban and rural differences should be considered in terms of the communication strategies and decision making during a pandemic. This research also presents a framework to investigate time-sensitive public sentiment at the county and state levels, which could guide local and state governments and regional communities in making decisions and developing policies in crises.
在新冠疫情期间，美国公共卫生当局以及县、州和联邦政府建议或下令采取某些预防措施，如佩戴口罩，以减少疾病传播。然而，个人对这些预防措施的反应各不相同。
本研究的目的是从时间和空间角度了解公众对新冠疫情以及建议或下令采取的预防措施的情绪变化，以及公众情绪变化与地理和社会经济因素之间的关系。
作者利用机器学习方法调查了2020年1月21日至2020年6月12日期间与新冠疫情相关推文的公众情绪极性。该研究测量了美国公众对新冠疫情一般话题和预防措施的情绪的时间变化和空间差异。
在时间分析中，我们发现了一个四阶段模式，从初始阶段的高负面情绪到第二和第三阶段的负面情绪下降和低落，再到最后阶段负面情绪的反弹和增加。我们还发现，城乡地区公众对预防措施的情绪存在显著差异，而贫困率和失业率与对新冠疫情问题的负面情绪呈正相关。
公众对新冠疫情和预防措施的情绪差异意味着未来疫情期间需要采取行动来应对初始阶段和反弹阶段。在疫情期间的沟通策略和决策方面应考虑城乡差异。本研究还提出了一个在县和州层面调查对时间敏感的公众情绪的框架，可为地方和州政府以及地区社区在危机中做出决策和制定政策提供指导。
