Semantic Network Analysis about Comments on Internet Articles about Nurse Workplace Bullying / 임상간호연구

ABSTRACT

PURPOSE@#A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis.@*METHODS@#From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as “nurse”, “Taeum”, and “bullying” were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence.@*RESULTS@#This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, “nurse”, “hospital”, “patient”, and “physician” were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters.@*CONCLUSION@#This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.