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dc.contributor.authorLee, Crystal Jayne
dc.contributor.authorYang, Tanya
dc.contributor.authorInchoco, Gabrielle D
dc.contributor.authorJones, Graham M.
dc.contributor.authorSatyanarayan, Arvind
dc.date.accessioned2021-07-26T14:52:14Z
dc.date.available2021-07-26T14:52:14Z
dc.date.issued2021-05
dc.identifier.isbn978-1-4503-8096-6
dc.identifier.urihttps://hdl.handle.net/1721.1/131130
dc.description.abstractControversial understandings of the coronavirus pandemic have turned data visualizations into a battleground. Defying public health officials, coronavirus skeptics on US social media spent much of 2020 creating data visualizations showing that the government's pandemic response was excessive and that the crisis was over. This paper investigates how pandemic visualizations circulated on social media, and shows that people who mistrust the scientific establishment often deploy the same rhetorics of data-driven decision-making used by experts, but to advocate for radical policy changes. Using a quantitative analysis of how visualizations spread on Twitter and an ethnographic approach to analyzing conversations about COVID data on Facebook, we document an epistemological gap that leads pro- and anti-mask groups to draw drastically different inferences from similar data. Ultimately, we argue that the deployment of COVID data visualizations reflect a deeper sociopolitical rift regarding the place of science in public life.en_US
dc.language.isoen
dc.publisherAssociation for Computing Machinery (ACM)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1145/3411764.3445211en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleViral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Onlineen_US
dc.typeArticleen_US
dc.identifier.citationLee, Crystal et al. "Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online." Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, May 2021, Yokohama, Japan, Association for Computing Machinery, May 2021. © 2021 The Authorsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journalProceedings of the 2021 CHI Conference on Human Factors in Computing Systemsen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-07-23T14:38:10Z
dspace.orderedauthorsLee, C; Yang, T; Inchoco, GD; Jones, GM; Satyanarayan, Aen_US
dspace.date.submission2021-07-23T14:38:11Z
mit.licensePUBLISHER_CC
mit.metadata.statusComplete


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