{"doi":"10.2196/preprints.30371","title":"The Associations Between Racially/Ethnically Stratified COVID-19 Tweets and COVID-19 Cases and Deaths: Cross-sectional Study (Preprint)","abstract":"<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> 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. </sec> <sec> <title>RESULTS</title> The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, an increase of 1 Asian tweet was correlated with 288 Asian cases (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001) and 93.4 Asian deaths (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001); an increase of 1 Black tweet was linked to 47.6 Black deaths (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001); an increase of 1 Latino tweet was linked to 719 Latino deaths (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001); and an increase of 1 White tweet was linked to 60.2 White deaths (&lt;i&gt;P&lt;/i&gt;&amp;lt;.001). </sec> <sec> <title>CONCLUSIONS</title> 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. </sec>","journal":null,"year":2021,"id":228412,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9427,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":382851,"name":"Bandana Kar","orcid":"0000-0002-0510-658X","position":1,"is_corresponding":false},{"id":382852,"name":"Francisco Alejandro Montiel Ishino","orcid":"0000-0002-2837-726X","position":2,"is_corresponding":false},{"id":446818,"name":"Tracy Onega","orcid":"0000-0002-1633-3040","position":3,"is_corresponding":false},{"id":382854,"name":"Faustine Williams","orcid":"0000-0001-5810-1291","position":4,"is_corresponding":false},{"id":382850,"name":"Xiaohui Liu","orcid":"0000-0002-4161-0388","position":0,"is_corresponding":true}],"reference_count":15,"raw_metadata":null,"created_at":"2026-07-18T23:54:50.414605Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}