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. 2023 Jul 27;9(8):e18766.
doi: 10.1016/j.heliyon.2023.e18766. eCollection 2023 Aug.

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

Cheongil Kim  1 , Jaesun Yeom  2 , Seunghoo Jeong  3 , Ji-Bum Chung  1
Affiliations

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

Cheongil Kim et al. Heliyon. .

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 resilience-related 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.

Keywords: Association rule mining; Bibliometric analysis; Research trends; Resilience; Resilience thinking; Social change.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Research framework for resilience-related trend analysis.
Fig. 2
Fig. 2
Number of resilience-related publications by year.
Fig. 3
Fig. 3
Number of validated rules (bar) and corresponding research categories (line).
Fig. 4
Fig. 4
Association rule mining (ARM)-based validated research topics - network representation.
Fig. 5
Fig. 5
Association rule mining (ARM)-based research topic trends from 2001 to 2020.
Fig. 6
Fig. 6
Association rule mining (ARM)-based research category trends from 2001 to 2020. Although the types of research categories have diversified, as explained in Fig. 3, several categories, such as Forestry, Oceanography, Plant Sciences, and Marine & Freshwater Biology, have received decreasing interest (Fig. 6b). Categories such as Energy & Fuels, Education Scientific Disciplines, Health Care Sciences & Services, Health Policy & Services, and Transportation have emerged as significant resilience-related societies since 2010 (Fig. 6c). Interest in Green & Sustainable Science & Technology was low prior to 2010 but has been ranked as an important category every year since 2011. As ARM-based research topics are linked to the corresponding research categories, we can understand in-depth resilience-related multidisciplinary research trends in terms of both topics and research categories over 20 years based on bibliographic data.
Fig. 7
Fig. 7
Expansion of resilience thinking based on identified research trends.
See this image and copyright information in PMC

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