Twitter sentiments on the stay-at-home orders in the United States
Topics:
Keywords: Sentiment analysis, Stay-at-Home orders, Geotweet, Twitter sentiments, Social media sentiments, The United States
Abstract Type: Virtual Paper Abstract
Authors:
Connor Yuhao Wu, Department of Geospatial Informatics, Troy University, Troy, AL, USA
Xinming Xia, School of Public Policy & Management, Tsinghua University, Beijing, China
Wenting Zhang, Department of Business Analytics and Information Systems, Auburn University, Auburn, AL, USA
Yi Zhang, Individualized Interdisciplinary Program (UGOD), The Hong Kong University of Science and Technology, Hong Kong, China
Lingbo Liu, Center of Geographic Analysis, Harvard University, Cambridge, MA, USA
Kejie Zhou, Department of Applied Economics, Fudan University, Shanghai, China
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Abstract
This study evaluated the effects of stay-at-home orders on Twitter sentiments in the United States during the COVID-19 pandemic. It aimed to understand the reactions of different groups, particularly vulnerable populations such as elderly individuals with medical conditions, people in rural areas, and low-income groups. Using a Twitter Sentiment Geographical Index based on 7.4 billion geotagged tweets, the study found that stay-at-home orders received a positive response and contributed to an improvement in Twitter sentiments. However, counties faced more significant difficulties in an urban (versus rural) setting, with a lower concentration of elderly individuals, or lower incomes during the pandemic. This study offers a sociological perspective, informed by large-scale Twitter data, for monitoring changes in public opinion, evaluating the impact of social events, and understanding the disaster management of pandemic shocks.
Twitter sentiments on the stay-at-home orders in the United States
Category
Virtual Paper Abstract
