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KMID : 0806120220520030291
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대한간호학회지
2022 Volume.52 No. 3 p.291 ~ p.307
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Images of Nurses Appeared in Media Report Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling
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Park Min-Young
Jeong Seok-Hee
Kim Hee-Sun
Lee Eun-Jee
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Abstract
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Purpose: To identify the main keywords, the network structure, and the main topics of press articles related to nurses appeared in media reports.
Methods: Data were media articles related to ‘nurse’ reported in a total of 16 central media for one year (Before COVID-19: July 1 - December 31, 2019; After COVID-19, January 1 - June 30, 2020). Data were collected in ‘Big Kinds’ database. A total of 7,800 articles was searched, and a total of 1,038 articles was used for the final analysis. Text network analysis and topic modeling were performed using Netminer 4.4 program.
Results: As a result of this study, the number of media reports related to nurses increased 3.86 times after the COVID-19 outbreak (6,197 cases) compared to before the COVID-19 outbreak (1,603 cases). As a result of topic modeling, four topics were derived before and after the COVID-19, respectively. Examples of topics were; Before the COVID-19 (‘a nurse who committed suicide because she could not withstand the Taewoom at work’, ‘a nurse as a perpetrator of the newborn abuse case’), and After the COVID-19 (‘a nurse as a victim of COVID-19’, ‘a nurse working with the support of the people’, ‘a nurse as a top contributor and a warrior to protect from COVID-19’).
Conclusion: Topics, derived by topic modelling, were changed more positively after the COVID-19, compared to before the COVID-19. It is suggested that continuous monitoring and further researches about the images of nurses should be performed by individual nurses and nursing organization.
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KEYWORD
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COVID-19, Nurses, Mass Media, Image, Social Network Analysis
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