Temporal Relationship between Daily Reports of COVID-19 Infections and Related GDELT and Tweet Mentions

Innocensia Owuor () and Hartwig H. Hochmair
Additional contact information
Innocensia Owuor: Geomatics Sciences, Fort Lauderdale Research and Education Center, University of Florida, Davie, FL 33314, USA
Hartwig H. Hochmair: Geomatics Sciences, Fort Lauderdale Research and Education Center, University of Florida, Davie, FL 33314, USA

Geographies, 2023, vol. 3, issue 3, 1-26

Abstract: Social media platforms are valuable data sources in the study of public reactions to events such as natural disasters and epidemics. This research assesses for selected countries around the globe the time lag between daily reports of COVID-19 cases and GDELT (Global Database of Events, Language, and Tone) and Twitter (X) COVID-19 mentions between February 2020 and April 2021 using time series analysis. Results show that GDELT articles and tweets preceded COVID-19 infections in Australia, Brazil, France, Greece, India, Italy, the U.S., Canada, Germany, and the U.K., while for Poland and the Philippines, tweets preceded and GDELT articles lagged behind COVID-19 disease incidences, respectively. This shows that the application of social media and news data for surveillance and management of pandemics needs to be assessed on a case-by-case basis for different countries. It also points towards the applicability of time series data analysis for only a limited number of countries due to strict data requirements (e.g., stationarity). A deviation from generally observed lag patterns in a country, i.e., periods with low COVID-19 infections but unusually high numbers of COVID-19-related GDELT articles or tweets, signals an anomaly. We use the seasonal hybrid extreme Studentized deviate test to detect such anomalies. This is followed by text analysis of news headlines from NewsBank and Google on the date of these anomalies to determine the probable event causing an anomaly, which includes elections, holidays, and protests.

Keywords: time series analysis; Twitter (X); cross-correlation; anomaly; pandemic (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2673-7086/3/3/31/pdf (application/pdf)
https://www.mdpi.com/2673-7086/3/3/31/ (text/html)

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:gam:jgeogr:v:3:y:2023:i:3:p:31-609:d:1241528

Access Statistics for this article

Geographies is currently edited by Ms. Fannie Xu

More articles in Geographies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().