The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study

Jiepin Cao, Chiyoung Lee, Wenyang Sun and Jennie C. De Gagne
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Jiepin Cao: School of Nursing, Duke University, Durham, NC 27710, USA
Chiyoung Lee: School of Nursing & Health Studies, University of Washington Bothell, Bothell, WA 98011, USA
Wenyang Sun: Department of Education, Culture & Society, The University of Utah, Salt Lake City, UT 84112, USA
Jennie C. De Gagne: School of Nursing, Duke University, Durham, NC 27710, USA

IJERPH, 2022, vol. 19, issue 7, 1-11

Abstract: Evidence-based intervention and policy strategies to address the recent surge of race-motivated hate crimes and other forms of racism against Asian Americans are essential; however, such efforts have been impeded by a lack of empirical knowledge, e.g., about racism, specifically aimed at the Asian American population. Our qualitative descriptive study sought to fill this gap by using a data-mining approach to examine the contents of tweets having the hashtag #StopAsianHate. We collected tweets during a two-week time frame starting on 20 May 2021, when President Joe Biden signed the COVID-19 Hate Crimes Act. Screening of the 31,665 tweets collected revealed that a total of 904 tweets were eligible for thematic analysis. Our analysis revealed five themes: “Asian hate is not new”, “Address the harm of racism”, “Get involved in #StopAsianHate”, “Appreciate the Asian American and Pacific Islander (AAPI) community’s culture, history, and contributions” and “Increase the visibility of the AAPI community.” Lessons learned from our findings can serve as a foundation for evidence-based strategies to address racism against Asian Americans both locally and globally.

Keywords: Asian Americans; data mining; social media; qualitative research (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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