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Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twit...

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Autores principales: Nguyen, Thu T., Criss, Shaniece, Dwivedi, Pallavi, Huang, Dina, Keralis, Jessica, Hsu, Erica, Phan, Lynn, Nguyen, Leah H., Yardi, Isha, Glymour, M. Maria, Allen, Amani M., Chae, David H., Gee, Gilbert C., Nguyen, Quynh C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579565/
https://www.ncbi.nlm.nih.gov/pubmed/32993005
http://dx.doi.org/10.3390/ijerph17197032
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author Nguyen, Thu T.
Criss, Shaniece
Dwivedi, Pallavi
Huang, Dina
Keralis, Jessica
Hsu, Erica
Phan, Lynn
Nguyen, Leah H.
Yardi, Isha
Glymour, M. Maria
Allen, Amani M.
Chae, David H.
Gee, Gilbert C.
Nguyen, Quynh C.
author_facet Nguyen, Thu T.
Criss, Shaniece
Dwivedi, Pallavi
Huang, Dina
Keralis, Jessica
Hsu, Erica
Phan, Lynn
Nguyen, Leah H.
Yardi, Isha
Glymour, M. Maria
Allen, Amani M.
Chae, David H.
Gee, Gilbert C.
Nguyen, Quynh C.
author_sort Nguyen, Thu T.
collection PubMed
description Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.
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spelling pubmed-75795652020-10-29 Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19 Nguyen, Thu T. Criss, Shaniece Dwivedi, Pallavi Huang, Dina Keralis, Jessica Hsu, Erica Phan, Lynn Nguyen, Leah H. Yardi, Isha Glymour, M. Maria Allen, Amani M. Chae, David H. Gee, Gilbert C. Nguyen, Quynh C. Int J Environ Res Public Health Article Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment. MDPI 2020-09-25 2020-10 /pmc/articles/PMC7579565/ /pubmed/32993005 http://dx.doi.org/10.3390/ijerph17197032 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nguyen, Thu T.
Criss, Shaniece
Dwivedi, Pallavi
Huang, Dina
Keralis, Jessica
Hsu, Erica
Phan, Lynn
Nguyen, Leah H.
Yardi, Isha
Glymour, M. Maria
Allen, Amani M.
Chae, David H.
Gee, Gilbert C.
Nguyen, Quynh C.
Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title_full Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title_fullStr Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title_full_unstemmed Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title_short Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
title_sort exploring u.s. shifts in anti-asian sentiment with the emergence of covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7579565/
https://www.ncbi.nlm.nih.gov/pubmed/32993005
http://dx.doi.org/10.3390/ijerph17197032
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