J Korean Acad Nurs.  2022 Jun;52(3):291-307. 10.4040/jkan.22002.

Images of Nurses Appeared in Media Reports Before and After Outbreak of COVID-19: Text Network Analysis and Topic Modeling

• Park MY
• Jeong SH
• Kim HS
• Lee EJ

• KMID: 2530745
• DOI: http://doi.org/10.4040/jkan.22002

Abstract

Purpose
The aims of study were to identify the main keywords, the network structure, and the main topics of press articles related to nurses that have appeared in media reports.
Methods
Data were media articles related to the topic “nurse” reported in 16 central media within a one-year period spanning July 1, 2019 to June 30, 2020. Data were collected from the Big Kinds database. A total of 7,800 articles were searched, and 1,038 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4.
Results
The number of media reports related to nurses increased by 3.86 times after the novel coronavirus (COVID-19) outbreak compared to prior. Pre- and post-COVID-19 network characteristics were density 0.002, 0.001; average degree 4.63, 4.92; and average distance 4.25, 4.01, respectively. Four topics were derived before and after the COVID-19 outbreak, respectively. Pre-COVID-19 example topics are “a nurse who committed suicide because she could not withstand the Taewoom at work” andf “a nurse as a perpetrator of a newborn abuse case,” while post-COVID-19 examples are “a nurse as a victim of COVID-19,” “a nurse working with the support of the people,” and “a nurse as a top contributor and a warrior to protect from COVID-19.”
Conclusion
Topic modeling shows that topics become more positive after the COVID-19 outbreak. Individual nurses and nursing organizations should continuously monitor and conduct further research on nurses’ image.

Keyword

COVID-19; Nurses; Mass Media; Social Network Analysis; Semantics