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Prejudice and hate directed toward Asian individuals has increased in prevalence and salience during the COVID-19 pandemic, with notable rises in physical violence. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media. In the present study, we investigated temporal and geographical patterns in social media content relevant to anti-Asian prejudice during the COVID-19 pandemic. Using the Twitter Data Collection API, we queried over 13 million tweets posted between January 30, 2020, and April 30, 2021, for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. In a series of descriptive analyses, we found differences in the frequency of negative and positive keywords based on geographic location. Using burst detection, we also identified distinct increases in negative and positive content in relation to key political tweets and events. These largely exploratory analyses shed light on the role of social media in the expression and proliferation of prejudice as well as positive responses online.
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Global Prevalence Patterns of Anti-Asian Prejudice on Twitter During the COVID-19 Pandemic
Anti-Asian prejudice increased during the COVID-19 pandemic, evidenced by a rise in physical attacks on individuals of Asian descent. Concurrently, as many governments enacted stay-at-home mandates, the spread of anti-Asian content increased in online spaces, including social media platforms such as Twitter. In the present study, we investigated temporal and geographic patterns in the prevalence of social media content relevant to anti-Asian prejudice within the U.S. and worldwide. Specifically, we used the Twitter Data Collection API to query over 13 million tweets posted during the first 15 months of the pandemic (i.e., from January 30, 2020 to April 30, 2021), for both negative (e.g., #kungflu) and positive (e.g., #stopAAPIhate) hashtags and keywords related to anti-Asian prejudice. Results of a range of exploratory and descriptive analyses offer novel insights. For instance, in the U.S., results from a burst analysis indicated that the prevalence of negative (anti-Asian) and positive (counter-hate) messages fluctuated over time in patterns that largely mirrored salient events relevant to COVID-19 (e.g., political tweets, highly-visible hate crimes targeting Asians). Other representative findings include geographic differences in the frequency of negative and positive keywords that shed light on the regions within the U.S. and the countries worldwide in which negative and positive messages were most frequent. Additional analyses revealed informative patterns in the prevalence of original tweets versus retweets, the co-occurrence of negative and positive content within a tweet, and fluctuations in content in relation to the number of new COVID-19 cases and reported COVID-related deaths. Together,
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- PAR ID:
- 10572823
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- Lecture Notes in Social Networks (LNSN)
- ISSN:
- 2190-5436
- Subject(s) / Keyword(s):
- Anti-Asian prejudice COVID-19 pandemic Twitter Counter-hate
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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