Accepted for/Published in: JMIR Formative Research
Date Submitted: May 11, 2021
Open Peer Review Period: May 11, 2021 - Jul 6, 2021
Date Accepted: Apr 14, 2022
Date Submitted to PubMed: May 10, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Racially/ethnically stratified COVID-19 tweets associated with COVID-19 cases and deaths
ABSTRACT
Background:
During the Coronavirus Disease 2019 (COVID-19) pandemic, social media traffic volume increased exponentially. Previously, Twitter was used as an informational surveillance tool to detect influenza outbreaks and proved to be an efficient and reliable tool.
Objective:
The objective of this paper is to spatially examine the association of 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 US posted in April 2020 from Twitter to examine the association of 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 was collected[1]. 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 deaths were mapped to reveal their spatial distribution pattern. 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, the increase of one Asian tweet was correlated to 288 Asian cases (p<0.05) and 93.4 Asian deaths (p<0.05); the increase of one Black tweet was linked to 47.6 Black deaths (p<0.05); the increase of one Latino tweet was linked to 719 Latino deaths (p<0.05); and the increase of one White tweet was linked to 60.2 White deaths (p<0.05).
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 among racial/ethnic groups. Clinical Trial: N/A
Citation
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