Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter

Thu T Nguyen; Shaniece Criss; Eli K Michaels; Rebekah I Cross; Jackson S Michaels; Pallavi Dwivedi; Dina Huang; Erica Hsu; Krishay Mukhija; Leah H Nguyen; Isha Yardi; Amani M Allen; Quynh C Nguyen; Gilbert C Gee

doi:10.1016/j.ssmph.2021.100922

Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter

SSM Popul Health. 2021 Sep 10:15:100922. doi: 10.1016/j.ssmph.2021.100922. eCollection 2021 Sep.

Authors

Thu T Nguyen^{1

2}, Shaniece Criss³, Eli K Michaels⁴, Rebekah I Cross⁵, Jackson S Michaels², Pallavi Dwivedi¹, Dina Huang¹, Erica Hsu⁶, Krishay Mukhija², Leah H Nguyen⁶, Isha Yardi⁶, Amani M Allen^{4

7}, Quynh C Nguyen¹, Gilbert C Gee⁵

Affiliations

¹ Department of Epidemiology & Biostatistics, University of Maryland School of Public Health, College Park, MD, 20742, USA.
² Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA.
³ Department of Health Sciences, Furman University, Greenville, SC, 29613, USA.
⁴ Division of Epidemiology, University of California, Berkeley, CA, 94704, USA.
⁵ Department of Community Health Sciences, University of California, Los Angeles, CA, 90095, USA.
⁶ Department of Public Health Science, University of Maryland, College Park, MD, 20742, USA.
⁷ Division of Community Health Sciences, University of California, Berkeley, CA, 94704, USA.

Abstract

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names "Ahmaud Arbery," "Breonna Taylor," and "George Floyd" were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019-September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4-9 to 33.66% in June 1-7) (p < 0.001), but this decline was temporary, lasting just a few weeks. Themes that emerged during the content analysis included discussion of race or racism in positive (14%) or negative (38%) tones, call for action related to racism (18%), and counter movement/arguments against racism-related changes (6%). Conclusion: Although there was a sharp decline in negative Black sentiment and increased public awareness of structural racism and desire for long-lasting social change, these shifts were transitory and returned to baseline after several weeks. Findings suggest that negative attitudes towards Black people remain deeply entrenched.

Keywords: Big data; Black lives matter; Machine learning; Racial attitudes; Racism.

Abstract

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