Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Song, Juyoung | - |
dc.contributor.author | Jin, Dal-Lae | - |
dc.contributor.author | Song, Tae Min | - |
dc.contributor.author | Lee, Sang Ho | - |
dc.date.accessioned | 2024-03-03T06:30:28Z | - |
dc.date.available | 2024-03-03T06:30:28Z | - |
dc.date.issued | 2023-05 | - |
dc.identifier.issn | 1661-7827 | - |
dc.identifier.issn | 1660-4601 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90520 | - |
dc.description.abstract | COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic. © 2023 by the authors. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/ijerph20095753 | - |
dc.identifier.bibliographicCitation | International Journal of Environmental Research and Public Health, v.20, no.9 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85159113517 | - |
dc.citation.title | International Journal of Environmental Research and Public Health | - |
dc.citation.volume | 20 | - |
dc.citation.number | 9 | - |
dc.type.docType | Article | - |
dc.publisher.location | Switzerland | - |
dc.subject.keywordAuthor | COVID-19 | - |
dc.subject.keywordAuthor | future signals | - |
dc.subject.keywordAuthor | information diffusion | - |
dc.subject.keywordAuthor | social big data | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.