TY  - JOUR
AU  - Liu, Xiaohui
AU  - Kar, Bandana
AU  - Montiel Ishino, Francisco Alejandro
AU  - Onega, Tracy
AU  - Williams, Faustine
PY  - 2022
DA  - 2022/5/30
TI  - The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study
JO  - JMIR Form Res
SP  - e30371
VL  - 6
IS  - 5
KW  - racial/ethnic stratification
KW  - geo-tagged COVID-19 tweets
KW  - racial/ethnic disparity
KW  - surveillance
AB  - 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. 
SN  - 2561-326X
UR  - https://formative.jmir.org/2022/5/e30371
UR  - https://doi.org/10.2196/30371
UR  - http://www.ncbi.nlm.nih.gov/pubmed/35537056
DO  - 10.2196/30371
ID  - info:doi/10.2196/30371
ER  -