Nowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment

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dc.contributor.authorAskay, David
dc.contributor.authorMolony, Declan
dc.contributor.authorGlanz, Hunter
dc.contributor.authorAlber, Julia
dc.date.accessioned2021-12-24T17:57:04Z
dc.date.available2021-12-24T17:57:04Z
dc.date.issued2022-01-04
dc.description.abstractReal-time access to information during a pandemic is crucial for mobilizing a response. A sentiment analysis of Twitter posts from the first 90 days of the COVID-19 pandemic was conducted. In particular, 2 million English tweets were collected from users in the United States that contained the word ‘covid’ between January 1, 2020 and March 31, 2020. Sentiments were used to model the new case and death counts using data from this time. The results of linear regression and k-nearest neighbors indicate that public sentiments on social media accurately predict both same-day and near future counts of both COVID-19 cases and deaths. Public health officials can use this knowledge to assist in responding to adverse public health events. Additionally, implications for future research and theorizing of social media’s impact on health behaviors are discussed.
dc.format.extent8 pages
dc.identifier.doi10.24251/HICSS.2022.514
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79850
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocia Media and Healthcare Technology
dc.subjectcases
dc.subjectcovid-19
dc.subjectprediction
dc.subjectsentiment analysis
dc.titleNowcasting and Forecasting COVID-19 Cases and Deaths Using Twitter Sentiment
dc.type.dcmitext

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