Covid-19 Research Trends in Social Work

LDA Topic Modeling Analysis in South Korea

Datos Bibliográficos

ID11215744
AutoresTaeJeong Park (0000-0002-7629-5453, Department of Social Welfare, Seoul Cyber University, Seoul, South Korea, autor de correspondencia)
Año2024
Volumen50
Número4
Páginas609-619
Fecha de publicación2024-07-03
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaJournal of Social Service Research (JOURNAL)
Identificadores de la revistaISSN: 0148-8376 • E-ISSN: 1540-7314
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/01488376.2024.2354528
OpenAlexW4397020201
IdiomaEN
Referencias citadas20

This study utilizes LDA topic modeling to examine research trends related to COVID-19 within the field of social work, analyzing these trends through peer-reviewed articles from the Korea Citation Index database. Latent Dirichlet Allocation (LDA) topic modeling, a statistical method for discovering abstract topics within a collection of documents, is applied to categorize and summarize the thematic concentration of the literature. Five themes have emerged: healthcare service and digitalized methods, exploring mental health status, qualitative approaches to social service program responses to COVID-19, evaluation of care services, and public service and program responses to COVID-19. Key findings reveal that the Korean social work academia focused on digital-based non-face-to-face services, evaluating the adequacy of public social services, and analyzing mental health and caregiving services during the pandemic. These findings indicate a reassessment of social work practices in response to the pandemic, underscoring the need to explore the challenges and opportunities presented by varied national responses. Additionally, the application of Python and machine learning in this research has shown significant benefits for social work studies, enabling a deeper analysis of complex, big data and facilitating more informed decision-making

2019-20 coronavirus outbreak · Coronavirus disease 2019 (COVID-19 · Economic growth · Economics · Outbreak · Pandemic · Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 · Work (physics · Computational and Text Analysis Methods · Diverse Approaches in Healthcare and Education Studies · Engineering · Medicine · Technology and Data Analysis · Virology · Social Work