@Article{info:doi/10.2196/19301,
author="Budhwani, Henna
and Sun, Ruoyan",
title="Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the ``Chinese virus'' on Twitter: Quantitative Analysis of Social Media Data",
journal="J Med Internet Res",
year="2020",
month="May",
day="6",
volume="22",
number="5",
pages="e19301",
keywords="COVID-19; coronavirus; Twitter; stigma; social media; public health",
abstract="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. ",
issn="1438-8871",
doi="10.2196/19301",
url="http://www.jmir.org/2020/5/e19301/",
url="https://doi.org/10.2196/19301",
url="http://www.ncbi.nlm.nih.gov/pubmed/32343669"
}