162 0

Full metadata record

DC FieldValueLanguage
dc.contributor.author김한성-
dc.date.accessioned2022-03-30T07:06:01Z-
dc.date.available2022-03-30T07:06:01Z-
dc.date.issued2020-07-
dc.identifier.citationSOCIAL SCIENCE COMPUTER REVIEW, article no. 0894439320914505en_US
dc.identifier.issn0894-4393-
dc.identifier.issn1552-8286-
dc.identifier.urihttps://journals.sagepub.com/doi/10.1177/0894439320914505-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/169575-
dc.description.abstractAnxiety is a pervasive emotional state that tends to arise in situations involving uncertainty due partly to social and contextual issues including competition, economic disparity, and social insecurity. Thus, distribution of aggregate emotions, such as in anxiety, may reveal an important picture of otherwise invisible social processes in which individuals interact with local and global opportunities, constraints, and potential threats. The aim of this study is to present a computational approach to the dynamic distribution of anxiety extracted from natural language expressions of users of Twitter, a popular global social media platform. We develop an unsupervised machine learning procedure based on a naive Bayes model to classify contents of anxiety, estimate the degree of anxiety, and construct a geographic map of spatiotemporal distribution of anxiety. To validate our mapping results, a multilevel statistical analysis was performed to examine how anxiety distribution is correlated with other district-level sociodemographic statistics such as rates of birth and early divorce. Implications for further research and extension are discussed.en_US
dc.description.sponsorshipThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government Ministry of Science and ICT (No. 2018R1A5A7059549).en_US
dc.language.isoenen_US
dc.publisherSAGE PUBLICATIONS INCen_US
dc.subjectanxietyen_US
dc.subjectspatiotemporal distributionen_US
dc.subjectmachine learningen_US
dc.subjectsocial mediaen_US
dc.subjectcomputational social sciencesen_US
dc.titleMapping social distress A computational approach to spatiotemporal distribution of anxietyen_US
dc.typeArticleen_US
dc.identifier.doi10.1177/0894439320914505-
dc.relation.page1-20-
dc.relation.journalSOCIAL SCIENCE COMPUTER REVIEW-
dc.contributor.googleauthorChoi, Yong Suk-
dc.contributor.googleauthorKim, Hansung-
dc.contributor.googleauthorSohn, Dongyoung-
dc.relation.code2020056701-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF SOCIAL SCIENCES[S]-
dc.sector.departmentDEPARTMENT OF SOCIOLOGY-
dc.identifier.pidhsk-
Appears in Collections:
COLLEGE OF SOCIAL SCIENCES[S](사회과학대학) > SOCIOLOGY(사회학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE