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Journal of Medical Internet Research logoLink to Journal of Medical Internet Research
. 2020 May 6;22(5):e19301. doi: 10.2196/19301

Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data

Henna Budhwani 1,✉, Ruoyan Sun 1
Editor: Gunther Eysenbach
Reviewed by: Edson Da Silva, Jon-Patrick Allem
PMCID: PMC7205030  PMID: 32343669

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.

Keywords: COVID-19, coronavirus, Twitter, stigma, social media, public health

Introduction

Stigma is the deleterious, structural force that devalues those who hold undesirable characteristics [1]. Stigma is a social process that occurs between groups; this process can occur in-person and online [2-6]. Regardless of setting, research has consistently found that stigma is associated with negative health outcomes [2,4,6-9]. For example, HIV-related stigma has pushed the HIV-epidemic underground, fueling ongoing transmission [10], and other disease-related stigmas are associated with negative health outcomes ranging from missed clinical visits to suicidal ideation [1,6,9]. There is evidence to show that stigma can become internalized, and internalized stigma can lead to distrust of health professionals, skepticism of public health systems, and an unwillingness to disclose behaviors related to transmission [2,8,9]. Because the coronavirus disease (COVID-19) is infectious, contact tracing is critically important to assessing community spread; thus, it is imperative that individuals trust their public health and health care systems so that they are willing to accept testing and, if diagnosed with COVD-19, report their whereabouts and activities. Therefore, creating and perpetuating stigma related to COVID-19 could be detrimental to public health efforts that require potentially stigmatized individuals to engage with their health systems.

On March 16, 2020, the president of the United States referred to the novel coronavirus as the “Chinese virus” on Twitter. He tweeted “The United States will be powerfully supporting those industries... that are particularly affected by the Chinese Virus...” After this presidential reference, a dialogue emerged examining if the phrase “Chinese virus” was xenophobic and stigmatizing, considering the availability of alternative scientific names such as coronavirus or COVID-19. Since stigma is created and perpetuated by society through social interaction and public commentary (eg, use of the term “Chinese virus” instead of scientific terms on Twitter), and stigma is reinforced by those in power (eg, use of the term “Chinese virus” by the US president), we hypothesized that there would be an increase in the frequency of the phrases “Chinese virus” and “China virus” on Twitter, comparing the prevalence of these phrases before and after the presidential reference.

Methods

Twitter

Twitter is an online social media platform where users send and receive short posts (maximum 280 characters) called tweets. Twitter currently has 152 million daily users, who produce about 500 million daily tweets [11].

Data, Tweets

We downloaded tweets from all 50 US states, using the Sysomos software (Sysomos, Inc). We extracted tweets that mentioned “Chinese virus” or “China virus” but did not contain “COVID-19” or “coronavirus.” The list of keywords referencing the “Chinese virus” are “Chinesevirus,” “Chinese virus,” “Chinavirus,” “China virus,” “#ChineseVirus19,” “#Chinesevirus,” “#ChineseVirusCorona,” and “#Chinavirus.” We excluded tweets containing the keywords “coronavirus,” “corona virus,” “COVID-19,” “COVID19,” “#COVID2019,” and “#corona.” By excluding tweets that contained both “Chinese virus” and “coronavirus,” we collated a sample of tweets that represented the intent of using “Chinese virus” in place of a scientific alternative, likely indicating deliberate stigmatization. We imputed the location of tweets based on Twitter users’ self-reported state of residence. Tweets posted between March 9 and March 15, 2020 (preperiod), were compared with tweets posted between March 19 and March 25, 2020 (postperiod). Original tweets and quote tweets (adding comments to an existing tweet) were included but not retweets (reposting of an existing tweet). Our final sample (N=193,862) contained all tweets posted in the pre- and postperiods by US-based Twitter users that exclusively mentioned a derivative of “Chinese virus.” Data extraction was conducted on April 10, 2020. Ethical approval was provided by the University of Alabama at Birmingham Institutional Review Board (IRB-#300005071).

Analysis

We used Stata 16 (StataCorp) to analyze our Twitter data and Python software (Python Software Foundation) to plot our state-level gradient 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 972.43% (n=160,792/16,535) increase. Comparatively, the number of tweets referencing COVID-19 in the preperiod and postperiod remained steady, at about 4.9 million tweets per period. A total of 13,569 (82.06%) of the preperiod and 145,521 (82.06%) of the postperiod tweets were associated with a Twitter user’s self-reported US state. Figure 1 is a heat map illustrating the state-by-state increases of tweets referencing “Chinese virus” or “China virus.” The darker the shade, the greater the increase. All 50 US states witnessed an increase in the number of tweets exclusively mentioning “Chinese virus” or “China virus” rather than COVID-19 or coronavirus. The 5 US states with the highest number of postperiod “Chinese virus” tweets were Pennsylvania, New York, Florida, Texas, and California. The 5 US states with the largest increase in pre- to postperiod “Chinese virus” tweets were Kansas, South Dakota, Mississippi, New Hampshire, and Idaho.

Figure 1.

Figure 1

Open in a new tab

Heat map of increases in tweets referencing “Chinese virus” or “China virus” across the United States.

In Table 1, we present US state-level results of tweets referencing “Chinese virus” or “China virus.” On average, at the state level, 271 such tweets were found in the preperiod and 2910 in the postperiod, indicating a ten-fold increase, similar to what we found at the national level. We also calculated the percentage increase and the prevalence increase. The percentage increase measures the percentage of all COVID-19 related tweets that mentioned “China virus” or “Chinese virus” exclusively. To account for variations in population size, prevalence of “Chinese virus” tweets per 10,000 people for each US state was calculated using the following formula: Inline graphic. State population sizes were taken from the 2019 US Census Bureau estimates [12]. On average, the state-level percentage increase was 997%, with a minimum of 661% and a maximum of 1447%. Similarly, the prevalence increase mean was 1015%, with a minimum of 734% and a maximum of 1456%. Large variations were found across US states, with the lowest postperiod prevalence of “Chinese virus” or “China virus” in South Dakota and the highest in Wyoming. The 5 US states with the highest prevalence of “Chinese virus” or “China virus” postperiod tweets were Arizona, New York, Florida, Nevada, and Wyoming.

Table 1.

Tweets referencing the novel coronavirus as “Chinese virus” or “China virus” by state.

States Preperiod Postperiod Change from pre- to postperiod

COVID-19 tweets, n “Chinese virus” tweets, n Percentage of tweetsa, (%) Prevalence of tweetsb COVID-19 tweets, n “Chinese virus” tweets, n Percentage of tweetsa, (%) Prevalence of tweetsb Percentage increasec (%) Prevalence increased (%)
AL 40,588 153 0.38 0.31 39,434 1749 4.44 3.57 1077 1043
AK 9251 40 0.43 0.55 9597 404 4.21 5.52 874 910
AZ 83,019 438 0.53 0.60 89,127 4256 4.78 5.85 805 872
AR 21,810 109 0.50 0.36 22,741 910 4.00 3.02 701 735
CA 696,645 1806 0.26 0.46 685,596 19,442 2.84 4.92 994 977
CO 84,092 291 0.35 0.51 85,014 3218 3.79 5.59 994 1006
CT 40,304 116 0.29 0.33 40,531 1253 3.09 3.51 974 980
DE 9789 31 0.32 0.32 10,095 304 3.01 3.12 851 881
FL 270,723 1243 0.46 0.58 294,652 13,070 4.44 6.09 866 951
GA 135,543 382 0.28 0.36 136,875 4192 3.06 3.95 987 997
HI 15,261 53 0.35 0.37 18,237 597 3.27 4.22 843 1026
ID 13,810 46 0.33 0.26 14,683 716 4.88 4.01 1364 1457
IL 176,425 410 0.23 0.32 169,849 4918 2.90 3.88 1146 1100
IN 58,767 192 0.33 0.29 57,218 2118 3.70 3.15 1033 1003
IA 27,552 71 0.26 0.23 27,917 847 3.03 2.68 1077 1093
KS 24,678 58 0.24 0.20 24,694 755 0.31 2.59 1201 1202
KY 45,648 179 0.39 0.40 45,841 1765 3.85 3.95 882 886
LA 51,734 151 0.29 0.32 48,623 1535 3.16 3.30 982 917
ME 16,948 54 0.32 0.40 17,762 520 2.93 3.87 819 863
MD 75,527 189 0.25 0.31 76,274 1932 2.53 3.20 912 922
MA 138,665 295 0.21 0.43 137,279 3201 2.33 4.64 996 985
MI 108,514 297 0.27 0.30 103,934 3623 3.49 3.63 1174 1120
MN 63,304 192 0.30 0.34 65,570 1882 2.87 3.34 846 880
MS 19,530 54 0.28 0.18 18,771 803 4.28 2.70 1447 1387
MO 68,869 201 0.29 0.33 71,951 2317 3.22 3.78 1003 1053
MT 9365 61 0.65 0.57 10,503 521 4.96 4.87 662 754
NE 19,791 54 0.27 0.28 18,840 670 3.56 3.46 1203 1141
NV 52,996 217 0.41 0.70 53,730 2377 4.42 7.72 980 995
NH 14,260 41 0.29 0.30 15,096 623 4.13 4.58 1335 1420
NJ 96,806 315 0.33 0.35 100,334 3823 3.81 4.30 1071 1114
NM 18,966 51 0.27 0.24 20,220 627 3.10 2.99 1053 1129
NY 487,901 1225 0.25 0.63 484,515 11,754 2.43 6.04 866 860
NC 110,832 327 0.30 0.31 115,394 3795 3.29 3.62 1015 1061
ND 5649 18 0.32 0.24 6148 193 3.14 2.53 885 972
OH 145,371 366 0.25 0.31 127,421 4613 3.62 3.95 1338 1160
OK 33,480 137 0.41 0.35 33,857 1436 4.24 3.63 937 948
OR 64,817 185 0.29 0.44 65,972 1985 3.01 4.71 954 973
PA 159,712 485 0.30 0.38 161,156 5249 3.26 4.10 973 982
RI 14,234 43 0.30 0.41 14,219 385 2.71 3.63 796 795
SC 43,104 222 0.52 0.43 46,251 2145 4.64 4.17 800 866
SD 6252 15 0.24 0.17 6573 200 3.04 2.26 1168 1233
TN 82,478 361 0.44 0.53 82,050 3431 4.18 5.02 855 850
TX 378,047 1442 0.38 0.50 369,006 14,861 4.03 5.13 956 931
UT 30,422 81 0.27 0.25 28,464 1004 3.53 3.13 1225 1140
VT 8625 18 0.21 0.29 9527 226 2.37 3.62 1037 1156
VA 97,602 301 0.31 0.35 104,176 3351 3.22 3.93 943 1013
WA 123,025 331 0.27 0.43 116,656 3316 2.84 4.35 957 902
WV 15,523 47 0.30 0.26 15,698 509 3.24 2.84 971 983
WI 51,670 130 0.25 0.22 52315 1593 3.05 2.74 1110 1125
WY 6185 45 0.73 0.78 6875 507 7.37 8.76 914 1027
Mean 87,482 271 0.33 0.38 87,545 2910 3.57 4.08 997 1015
Open in a new tab

aPercentage of all COVID-19 related tweets that mentioned “Chinese virus” or “China virus” exclusively.

bPrevalence of “Chinese virus” tweets per 10,000 people was calculated using the following formula: Inline graphic.

cPercentage of increase was calculated as: Inline graphic.

dPrevalence increase was calculated as: Inline graphic.

Discussion

Principal Result

We found notable increases in the use of the terms “Chinese virus” and “China virus” on Twitter at both the national and state levels by comparing these tweets (percentage and prevalence) both before and after the March 16, 2020, presidential reference. The following are examples of “Chinese virus” or “China virus” tweets:

  • Not parroting MSM's [main stream media’s] narrative. It's the #WuFlu #ChineseCoronaVirus #ChinaVirus”

  • “#ChinaVirus #ChinaLiesPeopleDie”

Limitations

The pandemic is currently underway, so Twitter data—both in quantity (quantitative) and content (qualitative)—are rapidly shifting. We were unable to screen for automatically generated tweets (bots) within this short report [13,14]. Geographic locations associated with Twitter accounts were self-reported; thus, it is possible that some Twitter users may have moved without updating their state location or may have reported a false state location.

Comparison With Prior Work

There is a growing body of academic literature that leverages Twitter data to assess trends in population health and public sentiment [15-17]. Chew and Eysenbach [18] conducted a seminal examination of knowledge translation using Twitter data during the H1N1 outbreak; they found the proportion of tweets using “H1N1” increased over time compared to the relative use of “swine flu,” suggesting that the media’s choice in terminology (shifting from using the term “swine flu” to “H1N1”) influenced public uptake. In addition, it is relevant that a recent publication by Logie and Turan [19] presented a narrative on how stigma can hurt the COVID-19 public health response. This short report was developed considering the findings from prior studies.

Future Research

Future research could evaluate and show that stigma mechanisms work online, validate if Twitter and social media data can be informative to epidemic surveillance and health communication, examine the extent that Twitter and social media data is reliable in informing public health efforts and social science research, and explore how Twitter users view COVID-19 and the COVID-19 public health response (eg, testing, linkage to care).

Additionally, although there is a growing body of research using tweets to examine aspects of the novel coronavirus [20-22], to our knowledge, no studies have included a comprehensive set of search terms, which may include phrases such as “ncov,” “covid,” “sars-cov,” and “rona,” in defining their samples. If data extraction is not comprehensive, we run the risk of missing emerging sentiments and terminology, such as referencing the novel coronavirus as the “China virus” or “Chinese virus,” and sociobehavioral outcomes related to these trends.

Conclusions

The rise in tweets citing “Chinese virus” or “China virus” instead of COVID-19 or the novel coronavirus after the presidential reference on Twitter, 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. Generally speaking, perpetuating COVID-19-related stigma by using the phrase “Chinese virus” could harm public health efforts related to addressing the pandemic, specifically inciting fear and increasing distrust of public health systems by Chinese and Asian Americans. If these stigmatizing terms persist as malicious synonyms for the novel coronavirus, reparative efforts may be required to restore trust by marginalized communities.

Acknowledgments

Research reported in this publication was supported by the University of Alabama at Birmingham School of Public Health Back of the Envelope (for RS) and the National Institute of Mental Health of the National Institutes of Health under Award Number 1K01MH116737 (for HB). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Abbreviations

COVID-19

coronavirus disease

Footnotes

Authors' Contributions: HB conceptualized this study, and RS conducted the data collection and analysis. Both authors contributed to manuscript development and writing.

Conflicts of Interest: None declared.

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