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dc.contributor.authorGupta, Marichi
dc.contributor.authorBansal, Aditya
dc.contributor.authorJain, Bhav
dc.contributor.authorRochelle, Jillian
dc.contributor.authorOak, Atharv
dc.contributor.authorJalali, Seyed Mohammad Javad
dc.date.accessioned2021-01-04T22:31:06Z
dc.date.available2021-01-04T22:31:06Z
dc.date.issued2021-01
dc.date.submitted2020-11
dc.identifier.issn1386-5056
dc.identifier.urihttps://hdl.handle.net/1721.1/128953
dc.description.abstractObjective: The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals’ perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users’ perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time. Materials and Methods: We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion. Results: We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather’s impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion. Discussion: There is no consensus among the public for weather’s potential impact. Earlier months were characterized by tweets that were uncertain of weather’s effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza’s seasonality, President Trump’s comments on weather’s effect, and social distancing. Conclusion: We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications.en_US
dc.publisherElsevier BVen_US
dc.relation.isversionofhttps://doi.org/10.1016/j.ijmedinf.2020.104340en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcemedRxiven_US
dc.titleWhether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptionsen_US
dc.typeArticleen_US
dc.identifier.citationGupta, Marichi et al. "Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users’ perceptions." International Journal of Medical Informatics 145 (January 2021): 104340 © 2020 Elsevier B.V.en_US
dc.contributor.departmentSloan School of Managementen_US
dc.relation.journalInternational Journal of Medical Informaticsen_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.date.submission2021-01-04T18:54:00Z
mit.journal.volume145en_US
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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