Abstract
Background and objective Despite the fact that the COVID-19 pandemic has increased the demand for mental health services, access has been limited, resulting in service gaps and severance. Agro-healing, which is expected to be utilized successfully to promote mental health for both individuals and communities, could be a solution. This study was conducted to provide basic data for revitalizing policies and research related to agro-healing by analyzing the trends in big data of online news articles over the last decade. Methods A total of 2,310 news articles related to agro-healing were collected from January 1, 2012 to December 31, 2021 by crawling Naver News. To extract nouns with practical meaning, the Okt morphological analysis of the KoNLPy module in Python 3.9 was employed. Semantic network analysis was conducted to validate degree centrality, betweenness centrality, and eigenvector centrality in order to understand the centrality and connectivity of significant keywords. The data was visualized using Gephi 0.9.2 by performing CONCOR analysis to generate clusters. Results The keywords with the highest degree centrality were agro-healing, followed by healing, care farm, vitality, RDA, citizens, and rural tourism. Agro-healing, Healing, stress, urban, disabilities, care farm, dementia, and rural area were highest in terms of betweenness centrality. The eigenvector centrality was highest in agro-healing, followed by vitality, healing, care farm, and effect. As a result of the CONCOR analysis, four clusters were identified: ‘agro-healing characteristics’, ‘agro-healing resources’, ‘agro-healing activities’, and ‘agro-healing target and effect’. Conclusion According to the findings, social expectations and need for agro-healing to improve public health became a significant part of the discourses. This research is expected to help determine future research and policy directions, as the vitality of agro-healing continues to provide national welfare services and seek sustainable growth in agricultural and rural areas.
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