TY - GEN
T1 - #WashTheHate
T2 - 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
AU - Wheeler, Brittany
AU - Jung, Seong
AU - Barioni, Maria Camila N.
AU - Purohit, Monika
AU - Hall, Deborah L.
AU - Silva, Yasin N.
N1 - Funding Information:
This work was supported by the National Science Foundation under awards #2036127 and #2227488. Additionally, the authors would like to thank Johnny Hudson (Arizona State University) for his contributions to an early version of the Twitter timeline.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - AAPI
KW - COVID-19
KW - Twitter
KW - racism
KW - social media
UR - http://www.scopus.com/inward/record.url?scp=85151931996&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85151931996&partnerID=8YFLogxK
U2 - 10.1109/ASONAM55673.2022.10068578
DO - 10.1109/ASONAM55673.2022.10068578
M3 - Conference contribution
AN - SCOPUS:85151931996
T3 - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
SP - 484
EP - 491
BT - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
A2 - An, Jisun
A2 - Charalampos, Chelmis
A2 - Magdy, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 November 2022 through 13 November 2022
ER -