Influence of emotions on coping behaviors in crisis: a computational analysis of the COVID-19 outbreak

Hao Xu (), Smitha Muthya Sudheendra (), Jisu Huh (), Aadesh Salecha () and Jaideep Srivastava ()
Additional contact information
Hao Xu: The University of Melbourne
Smitha Muthya Sudheendra: University of Minnesota
Jisu Huh: University of Minnesota
Aadesh Salecha: Stanford University
Jaideep Srivastava: University of Minnesota

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 17, 1599-1623

Abstract: Abstract Widespread public crises often give rise to the proliferation of sensationalized rumors and conspiracy theories, which can evoke a variety of public emotions. Despite the growing importance of research on the relationship between emotions and coping behaviors in crisis, a dearth of natural observation-based investigation has been limiting theory development. To address this gap, this study conducted computational research to study the U.S. public’s discrete emotions and coping behaviors during the COVID-19 outbreak crisis, analyzing Twitter data, Google Trends data, and Google Community Mobility data. The results revealed that anger and fear were relatively more prominent emotions experienced by the public than other discrete emotions. Regarding the impacts of emotions on coping behaviors, it was found that the prevalence of low-certainty and retreat emotions was related to increased information-seeking and information-transmitting behaviors. Also, the prevalence of both high-certainty and low-certainty emotions during the COVID-19 outbreak was positively related to the public’s compliance with public health recommendations.

Keywords: Public health crisis; Crisis communication; Discrete emotions; Coping behaviors; Data mining (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-024-00282-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:7:y:2024:i:2:d:10.1007_s42001-024-00282-7

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-024-00282-7

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().