What’s in a Virus’ Name? From the “Chinese Virus” to Anti-Asian Racial Animus

by Sophie SHENG and Runjing LU

In a news conference on March 18, 2020, a reporter questioned then-President Trump on his use of the term “Chinese Virus.” In asking that question, the reporter drew attention to the increase in incidents of anti-Asian bias and violence as the pandemic spread across the United States.

Reports of anti-Asian hate and violence have grown sharply since the start of the pandemic. According to data from the Asian Pacific Policy and Planning Council, hate incidents spiked in March 2020. To address this situation, President Biden issued a presidential memorandum in January 2021 condemning racism, xenophobia, and intolerance against Asians and Pacific Islanders in the United States.

How does a pandemic that affects everyone triggers racial animus against a particular group? More specifically, what is the effect of COVID-19 on Americans’ attitudes towards Asians living in the United States? Could the mere association of the disease with an ethnic group cause an increase in the incidents of hate and bias against that group?

Trump’s notes showed he replaced “Corona” with “Chinese” Virus for his remarks at the White House on March 19, 2020.

In this blogpost based on our recent paper, we tackle the question of how to measure racial animus and investigate its relationship with hate crimes.  

We measure racial animus against Asians in the United States by focusing on the use of the c-word (“chink” or “chinks”) in Google searches and tweets. The c-word is the most salient and unambiguously pejorative racial slur against Asians, which accounts for both hidden and public animus in the United States. Google searches and tweets that include the epithet are mostly negative. For instance, the phrases “chinked eye” and “chink virus” are commonly found in Google queries and tweets that contain the c-word. Using data before the analysis sample, we show that Google search volume is a good proxy for measuring racial animus. Before the pandemic, for example, an area’s monthly Google searches for the epithet are positively correlated with monthly anti-Asian hate crimes, and negatively correlated with monthly visits to the Chinese restaurants.

First Local COVID-19 Diagnosis Leads to a Surge in Racial Animus Against Asians

In order to estimate the causal effect of COVID-19 on racial attitudes, we designed a quasi-random experiment in our study by leveraging different timing of the first COVID-19 diagnosis in each local area.

Our results show that the pandemic led to a surge in racial animus against Asians. In the week after the first local COVID-19 diagnosis, an area’s racially charged Google search rate increased by 22.6 percent. An area’s racially charged Twitter post rate increased by 130 percent. The rate of racially charged searches and tweets decreased only slightly over the following weeks. Based on what we know about the links between racially charged online content and hate crimes, and given the correlation based on historical data, we estimate that the increase in racially charged Google search rates would imply an 8.5 percent increase in anti-Asian hate crimes, in the absence of stay-at-home orders, in the first month after the first local diagnosis.

Figure 1. The Effect of the First Local COVID-19 Diagnosis on Local Racial Animus Rescaled Google Search Index

A Sharp Increase in First-time Racial Epithet Users Indicates a Real Attitudinal Change Toward Asians

We also find evidence that the observed effect represents a real change in attitude towards Asians. The increase in racially charged tweets mainly came from existing Twitter users who posted racial epithets for the first time, rather than from users who had previously used the term. Evidence also shows that hate incidents spiked in March 2020, and the proportion of c-word tweets tagged with anger and disgust increased from 23.3 to 40.8 percent after the first local diagnosis. Combined, they suggest that there is a change in racial attitudes rather than just a change in social norms that had become more accepting of expressions of racial animus.

Figure 2. The Effect of the First Local COVID-19 Diagnosis on Racially Charged Tweets by First-time and Existing C-word Users

Words Matter More Than a Bad Economy in Foreshadowing Hate Speech and Incidents of Bias

Figure 3. Hourly Relationship between Racial Animus and President Trump’s Tweet

COVID-19 takes a toll not only on lives, but also on livelihoods. Could it be that the increase in racial or ethnic animus originated from the economic downturn? Our study finds little evidence to support this hypothesis. Areas with more severe economic damage from the pandemic did not exhibit a larger increase in racial animus than areas with less severe impact.

Rather, our study finds that the key driver of anti-Asian incidents is the salience of the connection between COVID-19 and the Asian population. Using President Trump’s simultaneous mentioning of COVID-19 and China as a proxy for the salience of the connection, we found that one additional China-and-COVID tweet of President Trump corresponded to 20 percent more racially charged tweets nationwide in the four hours afterwards. Notably, we also find that one additional such tweet corresponded to an eight percentage point increase in offline anti-Asian hate incidents nationwide on the same day.

Taken together, we find that the salience of the connection between the disease and the Asian population has played a larger role than the negative economic impact of the disease in motivating racial animus during the pandemic. Recent reports point to continuing trend of Covid “hate crimes” against Asian Americans. Informing the public about the history and contemporary hate and violence against Asian Americans is imperative. Our results suggest that educating the public about the pandemic’s spread while refuting any purported connection between the disease and a particular racial or ethnic group should be part of an effective strategy to curb racial animus and to combat anti-Asian hate and violence.


Sophie SHENG is a PhD candidate in economics at UC San Diego.

Runjing LU received her PhD in economics from UC San Diego and is now an assistant professor of finance at Alberta School of Business.

Cover Picture by multimedia artist Amanda Phingbodhipakkiya for “I Still Believe in Our City,” a “public awareness campaign developed with the NYC Commission on Human Rights to combat anti-Asian discrimination, harassment, and bias as a result of COVID-19, and launched with the support of the NYC Department of Cultural Affairs.” Source: NYC Commission on Human Rights, The City of New York

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