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COVID-19: adverse population sentiment and place-based associations with socioeconomic and demographic factors

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Abstract

During the COVID-19 pandemic, increased adverse sentiment such as, fear, panic, anxiety was observed among the public in the United States of America (USA) apart from physical suffering and death. Authorities may find guidance for anticipation and explanation of such secondary threats by analyzing population sentiment on social media. We performed sentiment analysis (SA) using georeferenced tweets in the contiguous USA during the first two waves of COVID-19 (01 November 2019–15 September 2020). We classified the tweets into “adverse” and “non-adverse” sentiment and computed daily counts for both classes at the county-level. Utilizing clustering and Bayesian regression approaches, we analyzed the place-based demographic and socioeconomic covariates of sentiment. We detected 12 clusters that exhibited elevated adverse sentiment and discovered that higher unemployment, male population, and poverty was associated with increased odds of adverse sentiment in Tweets. Conversely, counties with higher COVID-19 case rates, rurality, and elderly population were associated with reduced odds. Pandemic preparedness, response and mitigation measures may benefit from knowledge of the geography of adverse sentiment. Combining spatial clustering and regression benefits the understanding COVID-19, as well as epidemiology in general.

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Data availability

Due to our agreement with Twitter, we cannot share the original tweets used in our study. However, all other datasets generated and/or analyzed are available in this repository, https://github.com/alexandster/covid19sentiment.

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Acknowledgements

This study was funded by the Immunology, Inflammation and Infectious Diseases Initiative and the Office of the Vice President for Research of the University of Utah. The authors would like to thank Dr. Simon Brewer for advice on questions regarding methodology.

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Authors and Affiliations

  1. Department of Geography, University of Utah, 260 S Central Campus Dr, RM 4625, Salt Lake City, UT, 84112, USA

    Alexander Hohl, Moongi Choi, Richard Medina & Neng Wan

  2. Department of Sociology, The University of Hong Kong, Room 928, 9/F., The Jockey Club Tower, Pokfulam Road, Pokfulam, Hong Kong SAR, China

    Ming Wen

  3. Department of Sociology, University of Utah, 380 S 1530 E RM 301, Salt Lake City, UT, 84112, USA

    Ming Wen

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  1. Alexander Hohl
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Correspondence to Alexander Hohl.

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Hohl, A., Choi, M., Medina, R. et al. COVID-19: adverse population sentiment and place-based associations with socioeconomic and demographic factors. Spat. Inf. Res. 32, 73–84 (2024). https://doi.org/10.1007/s41324-023-00544-y

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