AAG 2023 Annual Meeting

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

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.

A link to the presentation, viewable with AAG member log in: 

https://aag.secure-platform.com/aag2023/solicitations/39/sessiongallery/...