#  Twitter sentiments on the stay-at-home orders in the United States 

 



####  calendar\_today Date and Time 

 **March 26, 2023** 

 12:20PM - 12:20PM EDT 

####  pin\_drop Location 

 **Denver, CO**  



 

 



 

 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…](https://aag.secure-platform.com/aag2023/solicitations/39/sessiongallery/5739/application/21325)



 

 



 

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