Creating Covid-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter

Quantitative Analysis of Social Media Data

Datos Bibliográficos

ID23353480
AutoresHenna Budhwani (0000-0002-6716-9754, University of Alabama at Birmingham, autor de correspondencia), Ruoyan Sun (0000-0001-8412-7727, University of Alabama at Birmingham)
Año2020
Volumen22
Número5
Páginase19301
Fecha de publicación2020-05-06
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Medical Internet Research (JOURNAL)
Identificadores de la revistaISSN: 1438-8871 • E-ISSN: 1438-8871
EditorialJMIR Publications Inc. (PUBLISHER • CA)
DOI10.2196/19301
PMID32343669
OpenAlexW3017885019
IdiomaEN
Citas recibidas90
Referencias citadas21

BACKGROUND: Stigma is the deleterious, structural force that devalues members of groups that hold undesirable characteristics. Since stigma is created and reinforced by society-through in-person and online social interactions-referencing the novel coronavirus as the "Chinese virus" or "China virus" has the potential to create and perpetuate stigma. OBJECTIVE: The aim of this study was to assess if there was an increase in the prevalence and frequency of the phrases "Chinese virus" and "China virus" on Twitter after the March 16, 2020, US presidential reference of this term. METHODS: Using the Sysomos software (Sysomos, Inc), we extracted tweets from the United States using a list of keywords that were derivatives of "Chinese virus." We compared tweets at the national and state levels posted between March 9 and March 15 (preperiod) with those posted between March 19 and March 25 (postperiod). We used Stata 16 (StataCorp) for quantitative analysis, and Python (Python Software Foundation) to plot a state-level heat map. RESULTS: A total of 16,535 "Chinese virus" or "China virus" tweets were identified in the preperiod, and 177,327 tweets were identified in the postperiod, illustrating a nearly ten-fold increase at the national level. All 50 states witnessed an increase in the number of tweets exclusively mentioning "Chinese virus" or "China virus" instead of coronavirus disease (COVID-19) or coronavirus. On average, 0.38 tweets referencing "Chinese virus" or "China virus" were posted per 10,000 people at the state level in the preperiod, and 4.08 of these stigmatizing tweets were posted in the postperiod, also indicating a ten-fold increase. The 5 states with the highest number of postperiod "Chinese virus" tweets were Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), and California (n=19,442). Adjusting for population size, the 5 states with the highest prevalence of postperiod "Chinese virus" tweets were Arizona (5.85), New York (6.04), Florida (6.09), Nevada (7.72), and Wyoming (8.76). The 5 states with the largest increase in pre- to postperiod "Chinese virus" tweets were Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), New Hampshire (n=582/41, 1420%), and Idaho (n=670/46, 1457%). CONCLUSIONS: The rise in tweets referencing "Chinese virus" or "China virus," along with the content of these tweets, indicate that knowledge translation may be occurring online and COVID-19 stigma is likely being perpetuated on Twitter.

China · Coronavirus disease 2019 (COVID-19) · Disease · Geography · Infectious disease (medical specialty) · Python (programming language) · Social media · Virus · World Wide Web · Computational and Text Analysis Methods · Computer Science · Data-Driven Disease Surveillance · Medicine · Misinformation and Its Impacts · Virology