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Research Papers
- Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing
- Ha, Ju-Young , Park, Hyo-Jin
- J Korean Acad Nurs 2023;53(1):55-68. Published online February 28, 2023
- DOI: https://doi.org/10.4040/jkan.22117
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Abstract
PDF - Purpose
The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing.
Methods
After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4.
Results
As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were ’education,’ ‘medical robot,’ and ‘fourth industry.’ Five topics were derived from news articles related to artificial intelligence and nursing: ‘Artificial intelligence nursing research and development in the health and medical field,’ ‘Education using artificial intelligence for children and youth care,’ ‘Nursing robot for older adults care,’ ‘Community care policy and artificial intelligence,’ and ‘Smart care technology in an aging society.’ Conclusion: The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.
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- 1 Web of Science
- Topic Modeling and Keyword Network Analysis of News Articles Related to Nurses before and after “the Thanks to You Challenge” during the COVID-19 Pandemic
- Yun, Eun Kyoung , Kim, Jung Ok , Byun, Hye Min , Lee, Guk Geun
- J Korean Acad Nurs 2021;51(4):442-453. Published online August 31, 2021
- DOI: https://doi.org/10.4040/jkan.20287
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Abstract
PDF
- Purpose
This study was conducted to assess public awareness and policy challenges faced by practicing nurses.
Methods
After collecting nurse-related news articles published before and after ‘the Thanks to You Challenge’ campaign (between December 31, 2019, and July 15, 2020), keywords were extracted via preprocessing. A three-step method keyword analysis, latent Dirichlet allocation topic modeling, and keyword network analysis was used to examine the text and the structure of the selected news articles.
Results
Top 30 keywords with similar occurrences were collected before and after the campaign. The five dominant topics before the campaign were: pandemic, infection of medical staff, local transmission, medical resources, and return of overseas Koreans. After the campaign, the topics ‘infection of medical staff’ and ‘return of overseas Koreans’ disappeared, but ‘the Thanks to You Challenge’ emerged as a dominant topic. A keyword network analysis revealed that the word of nurse was linked with keywords like thanks and campaign, through the word of sacrifice. These words formed interrelated domains of ‘the Thanks to You Challenge’ topic.
Conclusion
The findings of this study can provide useful information for understanding various issues and social perspectives on COVID-19 nursing. The major themes of news reports lagged behind the real problems faced by nurses in COVID-19 crisis. While the press tends to focus on heroism and whole society, issues and policies mutually beneficial to public and nursing need to be further explored and enhanced by nurses.
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- 6 Web of Science
Original Article
- Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service
- Minji Kim, Mona Choi, Yoosik Youm
- J Korean Acad Nurs 2017;47(6):806-816. Published online January 15, 2017
- DOI: https://doi.org/10.4040/jkan.2017.47.6.806
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Abstract
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Abstract Purpose As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.
Methods The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword ‘comprehensive nursing care service’ using Python. A morphological analysis was performed using KoNLPy. Nodes on a ‘comprehensive nursing care service’ cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.
Results A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, ‘nursing workforce’ and ‘nursing service’ were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were ‘National Health Insurance Service’ and ‘comprehensive nursing care service hospital.’ The nodes with the highest edge weight were ‘national health insurance,’ ‘wards without caregiver presence,’ and ‘caregiving costs.’ ‘National Health Insurance Service’ was highest in degree centrality.
Conclusion This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.
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