Did the public attribute the Flint Water Crisis to racism as it was happening? Text analysis of Twitter data to examine causal attributions to racism during a public health crisis

Neslihan Bisgin, Halil Bisgin, Daniel Hummel, Jon Zelner and Belinda L. Needham ()
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Neslihan Bisgin: University of Michigan
Halil Bisgin: University of Michigan Flint
Daniel Hummel: University of Louisiana Monroe
Jon Zelner: University of Michigan
Belinda L. Needham: University of Michigan

Journal of Computational Social Science, 2023, vol. 6, issue 1, No 5, 165-190

Abstract: Abstract The Flint Water Crisis (FWC) was an avoidable public health disaster that profoundly affected the city’s residents, a majority of whom are Black. Although many scholars and journalists have called attention to the role of racism in the water crisis, little is known about the extent to which the public attributed the FWC to racism as it was unfolding. In this study, we used natural language processing to analyze nearly six million Flint-related tweets posted between April 1, 2014, and June 1, 2016. We found that key developments in the FWC corresponded to increases in the number and percentage of tweets that mentioned terms related to race and racism. Similar patterns were found for other topics hypothesized to be related to the water crisis, including water and politics. Using sentiment analysis, we found that tweets with a negative polarity score were more common in the subset of tweets that mentioned terms related to race and racism when compared to the full set of tweets. Next, we found that word pairs that included terms related to race and racism first appeared after the January 2016 state and federal emergency declarations and a corresponding increase in media coverage of the FWC. We conclude that many Twitter users connected the events of the water crisis to race and racism in real-time. Given growing evidence of negative health effects of second-hand exposure to racism, this may have implications for understanding minority health and health disparities in the US.

Keywords: Flint Water Crisis; Natural language processing; Racism; Tweets (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-022-00192-6

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