Document Type
Article
Publication Date
10-9-2024
Publication Title
Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection
Pages
235–266
Publisher Name
Springer Nature Switzerland AG
Publisher Location
Cham, Switzerland
Abstract
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, these findings underscore the value of research examining trends in social media messages of hate and counter-hate during the COVID-19 pandemic.
Recommended Citation
Wheeler, B. et al. (2024). Global Prevalence Patterns of Anti-Asian Prejudice on Twitter During the COVID-19 Pandemic. In: Kaya, M., Alhajj, S., Sailunaz, K., Day, MY. (eds) Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-031-75204-9_10
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Copyright Statement
© Springer Nature Switzerland AG, 2024.
Comments
Author Posting © The Author(s), 2026. This article is posted here by permission of Springer Nature Switzerland AG for personal use and non-commercial redistribution. This article was published open access in Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection (October 2024), https://doi.org/10.1007/978-3-031-75204-9_10.