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dc.contributor.authorChai, Yuchen
dc.contributor.authorPalacios, Juan
dc.contributor.authorWang, Jianghao
dc.contributor.authorFan, Yichun
dc.contributor.authorZheng, Siqi
dc.date.accessioned2023-01-18T14:49:16Z
dc.date.available2023-01-18T14:49:16Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/1721.1/147186
dc.description.abstract<jats:p>COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people’s daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders (“nightmare” and “insomnia”) take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.</jats:p>en_US
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)en_US
dc.relation.isversionof10.1371/journal.pone.0278322en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourcePLoSen_US
dc.titleMeasuring daily-life fear perception change: A computational study in the context of COVID-19en_US
dc.typeArticleen_US
dc.identifier.citationChai, Yuchen, Palacios, Juan, Wang, Jianghao, Fan, Yichun and Zheng, Siqi. 2022. "Measuring daily-life fear perception change: A computational study in the context of COVID-19." PLOS ONE, 17 (12).
dc.contributor.departmentMassachusetts Institute of Technology. Department of Urban Studies and Planningen_US
dc.relation.journalPLOS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2023-01-18T14:42:17Z
dspace.orderedauthorsChai, Y; Palacios, J; Wang, J; Fan, Y; Zheng, Sen_US
dspace.date.submission2023-01-18T14:42:18Z
mit.journal.volume17en_US
mit.journal.issue12en_US
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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