Spatial distribution of anti-Asian hate tweets under COVID-19
Spatial distribution of anti-Asian hate tweets under COVID-19

Spatial distribution of anti-Asian hate tweets under COVID-19

The Spatial Distribution of Hateful Tweets Against Asians and Asian Americans: USA, November 2019-May 2020. Credit: University of Utah

In January 2020, SARS-CoV-2 reached the United States. With it came an even faster-spreading virus – xenophobic rhetoric referring to the epicenter of the pandemic in Wuhan, China. Politicians flooded news media and social media with distrust of the Chinese government, branding COVID-19 as “Chinese flu”, “Wuhan flu”, “Kung flu” and more.

The message blaming COVID-19 on its Asian country of origin seemed to be pouring into the streets. Americans who are Asian, Asian Americans, and Pacific Islanders (AAPIs) began to experience racist harassment, discrimination, and physical assault at higher rates than before the pandemic. According to Stop the AAPI Hate Initiative, an online tool for self-reported hate incidents against AAPI individuals, there were 9,000 hate incidents during the first year of COVID-19. In our online age, it is crucial to understand whether racist tendencies on social media cause harm in the real world. If so, how can we best predict where racist attacks are most likely to occur?

A new University of Utah-led study has taken a first step in a paper published in American Journal of Public Health in April. The researchers mapped anti-Asian hatred using Twitter’s dataset of geolocated tweets that contained keywords that reflected COVID-19 and anti-Asian hatred in November 2019 and May 2020.

They found that anti-Asian hate speech increased between January and March 2020 with clusters of hateful tweets scattered across the adjacent United States, varying in size, strength distribution, and location. The authors identified two peaks in hateful tweets: The first in late January 2020, when COVID-19 first came to the United States; the other in mid-March, after President Donald Trump tweeted about the “Wuhan flu” and “Chinese virus.” Eventually, the amount of anti-Asian tweets dropped, but remained higher than before the pandemic.

“We can interpret that as once the hatred is up, the hatred continues. At least as long as our study period,” said Alexander Hohl, an associate professor at the University of Utah and lead author of the study. “During the beginning of COVID, there were reports of hate crimes committed by Asians and Asian Americans in the United States, we believe that justifies a research perspective.”

This study is the first to put anti-Asian hate tweets on a map. The authors hope that this method will one day help guide officials to allocate resources to respond to pandemic-based racism as a threat to public health.

“In American culture, we perceive Asians to be the model minority, right? They tend to do well academically and make money, or at least that’s our view,” said Richard Medina, an associate professor at the University of Utah and co-author of the Study. “Asians in America are often overlooked as targets for hate speech or hate crimes. But COVID-19 brought it out.”

The authors purchased 4,234,694 geolocated tweets from Twitter. From this dataset, they searched for tweets that met the following criteria: written in English, sent within the study period, located in the contiguous United States, and including COVID-19 languages ​​(coronavirus, SARS-CoV-2, etc.). They then classified the remaining 3,274,614 tweets as hateful or non-hateful based on the presence of additional keywords related to anti-Asian hatred (kungflu, Wuhan virus, etc.) and put them on the map.

They identified 15 clusters as the geographical regions where the number of anti-Asian hate tweets was statistically higher than expected, based on the underlying population. The authors have also calculated the so-called relative risk for each U.S. county, which describes the relationship between hateful tweets sent within a county versus outside. The darker the color of the counties, the higher this relative risk.

The authors found low hatred from November 2019 to January 2020 (0% – 1% of daily tweets were hateful). Then the increase began, culminating with the first of two peaks in the study period.

The strongest cluster was in Ross County, Ohio, where the proportion of hateful tweets was about 300 times higher than the rest of the country. There were no identifiable patterns of anti-Asian hatred along urban and rural areas or geographical gradients; they were spread all over the country and varied greatly in size, population, location and number of tweets. The authors plan to make further analyzes that may reveal demographic and socioeconomic factors to explain cluster placements.

Racism is a public health crisis

The study is in the early stages of what the authors hope could become a public health tool to protect marginalized communities from racist hate crimes. Their next step is to improve their method of classifying hateful tweets and by exploring other algorithms to create clusters.

They will also understand whether hateful tweets mean that violence against the group will break out. They plan to map the time and place where hate crimes have taken place against AAPI, even though data on hate crimes are notoriously unreliable due to inconsistent reporting from states to federal databases.

“It’s hard to track hate because the FBI’s database of hate crimes contains so little of a range of incidents compared to what’s actually happening, especially because some forms of verbal harassment are not illegal. But there’s an increase in that, and it’s not just because we ‘are more aware,’ Medina said. “We are seeing more Asian immigrants across the country, and we are seeing more hatred towards them as an immigrant population in general. It will just continue, and we need to be aware of that. “

The authors also want to develop a description of a hateful place. They plan to look at the socio-economic and demographic variables in counties or cities with elevated levels of anti-Asian hatred.

“The hope is that we can use hatred Social Media as a prediction of street hatred for use as an alert to communities and public health authorities to get the help and protection they need. ”


COVID-19 political comment related to online hate crime


More information:
Alexander Hohl et al., Spatial distribution of hateful tweets against Asians and Asian Americans during the COVID-19 pandemic, November 2019 to May 2020, American Journal of Public Health (2022). DOI: 10.2105 / AJPH.2021.306653

Supplied by
University of Utah


Citation: Spatial distribution of anti-Asian hate tweets under COVID-19 (2022, May 25) retrieved May 25, 2022 from https://phys.org/news/2022-05-spatial-anti-asian-tweets-covid -.html

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