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http://dx.doi.org/10.9708/jksci.2021.26.08.091

Comparative Analysis of News Big Data related to SARS-CoV, MERS-CoV, and SARS-CoV-2 (COVID-19)  

Woo, Jae-Hyun (Dept. of Medical Devices Industry, Dongguk University)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.8, 2021 , pp. 91-101 More about this Journal
Abstract
This paper intends to draw implications for preparing for Post-Corona in the health field and policy fields as the global pandemic is experienced due to COVID-19. The purpose of this study is to analyze the news and trends of media companies through temporal analysis of the three infectious diseases, SARS-CoV, MERS-CoV, and SARS-CoV-2 (COVID-19), in which the domestic infectious disease preventive system was active throughout the first year of the outbreak. To this end, by using the news analysis program of the Korea Press Foundation 'Big Kinds', the number of news articles per year was digitized based on the period when each infectious disease had an impact on Korea, and major trends were implemented and analyzed in a word cloud. As a result of the analysis, the number of articles related to infectious diseases peaked when the World Health Organization (WHO) declared a warning and (suspicious) confirmed cases occurred. According to keyword and word cloud analysis, 'infectious disease outbreak and major epidemic areas', 'prevention authorities', and 'disease information and confirmed patient information' were found to be the main common features, and differences were derived from the three infectious diseases. In addition, the current status of the infodemic was identified by performing word cloud analysis on information in uncertainty. The results of this study are significant in that they were able to derive the roles of the health authorities and the media that should be preceded in the event of a new disease epidemic through previously experienced infectious diseases, and areas to be rearranged.
Keywords
SARS; MERS; COVID-19; NEWS; BIGKINDS;
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