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KMID : 0869920220280040444
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Journal of Korean Academy Society of Nursing Education
2022 Volume.28 No. 4 p.444 ~ p.455
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A topic modeling analysis for Korean online newspapers: Focusing on the social perceptions of nurses during the COVID-19 epidemic period
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Chang Soo-Jung
Park Sun-Ah
Son Ye-Dong
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Abstract
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Purpose: This study explored the meaning of the social perceptions of nurses in online news articles during the coronavirus disease 2019 (COVID-19) pandemic.
Methods: A total of 339 nurse-related articles published in Korean online newspapers from January 1 to December 31, 2020, were extracted by entering various combinations of OR and AND with the four words “Corona,” “COVID,” “Nursing,” and “Nurse” as search keywords using BIGKinds, a news database provided by the Korea Press Foundation. The collected data were analyzed with a keyword network analysis and topic modeling using NetMiner 4.
Results: The top keywords extracted from the nurse-related news articles were, in the following order, “metropolitan area,” “protective clothing,” “government,” “task,” and “admission.” Four topics representing keywords were identified: “encouragement for dedicated nurses,” “poor work environment,” “front-line nurses working with obligation during the COVID-19 pandemic,” and “nurses' efforts to prevent the spread of COVID-19.”
Conclusion: The media’s attention to the dedication of nurses, the shortage of nursing resources, and the need for government support is encouraging in that it forms the public opinion necessary to lead to substantial improvements in treating nurses. The nursing community should actively promote policy proposals to improve treatment toward nurses by utilizing the net function of the media and proactively seek and apply strategies to improve the image of nurses working in various fields.
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KEYWORD
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COVID-19, Newspaper article, Nurses, Data mining
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