Resilience and social change: Findings from research trends using association rule mining

Abstract

This study analyzed the historical development of resilience with respect to multidisciplinary aspects using association rule mining (ARM). ARM is a rule-based machine-learning approach tailored to identify validated relations among multiple variables in a large dataset. This study collected author keywords from all resilience-related literature in the Web of Science database and examined the changes in validated resilience-related topics using ARM. We found that resiliencerelated research tends to diversify and expand over time. Although topics and their academic fields related to engineering and complex adaptive systems were prominent in the early 2000s, psychosocial resilience and social-ecological resilience have received significant attention in recent years. The increasing interest in resilience-related topics linked to psychological and ecological factors, as well as social system components, can be attributed to the impact of a series of complex and global events that occurred in the late 2000s. Recently, resilience has been conceived as a way of thinking, perspective, or paradigm to address emergent complexity and uncertainty with vague concepts. Resilience is increasingly being regarded as a boundary spanner that promotes communication and collaboration among stakeholders who share different interests and scientific knowledge.