Analysis of the semantic network of post-traumatic stress disorder using Korean social big data

Abstract

Introduction: In this study, we wanted to examine how post-traumatic stress disorder was discussed in Korean newspaper articles with semantic network analysis suitable for unstructured big data analysis. Methods: This study analyzed 11,304 articles related to post-traumatic stress reported by four major Korean newspapers for three years from July 30, 2017, to July 30, 2020. R 3.6.2 program was used to calculate TF and TF-IDF values, and UCINET 6.0 and interlocked NetDraw were used for DC, EC, and CONCOR values. Results: As a result of deriving 50 major keywords with high TF-IDF values in newspaper articles related to a posttraumatic stress disorder, TF-IDF values were high in the order of 'sick leave', 'solitary confinement', 'detention center', 'standing order', and 'prisoner'. As a result of conducting a CONCOR analysis to determine which sub-clusters keywords are classified into, the researcher derived each cluster based on words included: 'PTSD by crops' (cluster 1), 'PTSD by broadcasting accidents' (clusters), 'PTSD by farm livestock accidents' (cluster 3), and 'PTSD by various accidents' (cluster 4). Conclusion: Based on the research results, post-traumatic stress disorder needs to be managed nationally. As such, we intend to provide basic data for policy development and intervention programs.