The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study

Xiaohui Liu; Bandana Kar; Francisco Alejandro Montiel Ishino; Tracy Onega; Faustine Williams

doi:10.2196/30371

The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study

JMIR Form Res. 2022 May 30;6(5):e30371. doi: 10.2196/30371.

Authors

Xiaohui Liu^{1

2}, Bandana Kar³, Francisco Alejandro Montiel Ishino^#¹, Tracy Onega², Faustine Williams¹

Affiliations

¹ National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States.
² Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, United States.
³ National Security Sciences Directorate, Oak Ridge National Lab, Knoxville, TN, United States.

^# Contributed equally.

PMID: 35537056
PMCID: PMC9153911
DOI: 10.2196/30371

Abstract

Background: The COVID-19 pandemic exacerbated existing racial/ethnic health disparities in the United States. Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on racial/ethnic minorities and help address health disparities.

Objective: This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic.

Methods: This cross-sectional study used geotagged COVID-19 tweets from within the United States posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups were collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group-related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and COVID-19 deaths were mapped to reveal their spatial distribution patterns. An ordinary least squares (OLS) regression model was applied to each stratified dataset.

Results: The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (P<.001) and 93.4 Asian deaths (P<.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (P<.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (P<.001); and an increase of 1 White tweet was linked to 60.2 White deaths (P<.001).

Conclusions: Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.

Keywords: geo-tagged COVID-19 tweets; racial/ethnic disparity; racial/ethnic stratification; surveillance.

©Xiaohui Liu, Bandana Kar, Francisco Alejandro Montiel Ishino, Tracy Onega, Faustine Williams. Originally published in JMIR Formative Research (https://formative.jmir.org), 30.05.2022.

Grants and funding

ZIA MD000015/ImNIH/Intramural NIH HHS/United States