Big Data Analysis of Terror Management Theory's Predictions in the COVID-19 Pandemic.

Publisher: Sage Publications Country of Publication: United States NLM ID: 1272106 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-3764 (Electronic) Linking ISSN: 00302228 NLM ISO Abbreviation: Omega (Westport) Subsets: MEDLINE

Publication: 2015- : Los Angeles Sage Publications
Original Publication: Westport, Conn., Greenwood Periodicals.

COVID-19*/epidemiology ; COVID-19*/psychology ; Big Data*; Humans ; Singapore ; Pandemics ; Psychological Theory ; SARS-CoV-2 ; Female ; Male ; Anxiety/psychology

The current study aimed to address the limitations of the terror management theory literature by using big data analysis to examine the theory's predictions in the COVID-19 pandemic. Specifically, Google Trends were examined before and after the first COVID-19 case was identified in Singapore. The results showed that there was a significant increase in mortality salience, intergroup conflict, and prosocial behavior, and a significant decrease in materialism after the first COVID-19 case was identified. However, no significant differences were found for anxiety. Limitations include the assumption that search terms reflect intentions that would eventually lead to a relevant behavior and the lack of data from other sources to corroborate with the results from Google Trends. Future research could use data from other sources to examine the effects of COVID-19 on theoretically relevant behaviors.
Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Keywords: COVID-19; big data analysis; google trends; mortality salience; terror management theory

Date Created: 20220420 Date Completed: 20240810 Latest Revision: 20240826

20240826

PMC9024090

10.1177/00302228221092583

35440220