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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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Neslihan Bisgin & Halil Bisgin & Daniel Hummel & Jon Zelner & Belinda L. Needham, 2023. "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," Journal of Computational Social Science, Springer, vol. 6(1), pages 165-190, April.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:1:d:10.1007_s42001-022-00192-6
    DOI: 10.1007/s42001-022-00192-6
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    Cited by:

    1. Shiyi Zhang & Panayiota Tsatsou & Lauren McLaren & Yimei Zhu, 2024. "Comparing location-specific and location-open social media data: methodological lessons from a study of blaming of minorities on Twitter during the COVID-19 pandemic," Journal of Computational Social Science, Springer, vol. 7(3), pages 2457-2479, December.

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