TY - JOUR
T1 - Computational text analysis of a scientific resilience management corpus
T2 - Environmental insights and implications
AU - Nassour, J.
AU - Leykin, D.
AU - Elhadad, M.
AU - Cohen, O.
N1 - Funding Information:
Acknowledgments. The authors would like to thank the group of professionals – both researchers and practitioners from the DARWIN project that conducted the systematic literature review and interview of diverse experts. In particular, we would like to note the D1.1 group members which include Woltjer R., Nevhage, B., Nilsson, S., Adini, B., Cohen, O., Aharonson-Daniel L., Goldberg A., Grøtan, T.O., Branlat, M., Moe, M Frøystad, C., and Herrera, I.; Special thanks are sent to Dr. Herrera for coordinating the DARWIN project and to Dr. Woltjer for overseeing the literature review. The research leading to the results received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 653289. Opinions expressed in this publication reflect only the authors’ view. The Agency is not responsible for any use that may be made of the information it contains.
Funding Information:
The authors would like to thank the group of professionals ? both researchers and practitioners from the DARWIN project that conducted the systematic literature review and interview of diverse experts. In particular, we would like to note the D1.1 group members which include Woltjer R., Nevhage, B., Nilsson, S., Adini, B., Cohen, O., Aharonson-Daniel L., Goldberg A., Gr?tan, T.O., Branlat, M., Moe, M Fr?ystad, C., and Herrera, I.; Special thanks are sent to Dr. Herrera for coordinating the DARWIN project and to Dr. Woltjer for overseeing the literature review. The research leading to the results received funding from the European Union?s Horizon 2020 research and innovation program under grant agreement No 653289. Opinions expressed in this publication reflect only the authors? view. The Agency is not responsible for any use that may be made of the information it contains.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Resilience is a multifaceted concept describing the ability to cope with change or disruption. Its importance in the era of emergency preparedness and response, combined with its multidisciplinary attributes, have led researches to study similarities and differences in the meaning of resilience across various fields. A systematic literature review, conducted in the field of resilience management by the DARWIN project, yielded a scientific corpus of 419 articles. In the present study, automated text-analysis approaches were used to investigate this corpus and generate insights, aiming at understanding resilience management. Three complementary computational analyses were employed: (a) topic modeling to understand the different topics or fields discussed in the articles; (b) concept maps to provide a synthetic view of key concepts in the domain and their relations; (c) psycho-linguistic analysis to identify significant psychological categories addressed in the corpus. The topic model identified four key topics: Environmental/Socioecological aspects, Organiza-tional/Operational aspects, Health, and Infrastructure/Resource Management. The concept map recognized concepts at a finer granularity level and depicted them into five main clusters with relations between them, reflecting key dimensions leading to resilience management. The psycho-linguistic analysis highlighted the importance of psychological processes within resilience management. This study identified important aspects that need to be addressed when designing resilience management frameworks, such as rehabilitation period and the role of public.
AB - Resilience is a multifaceted concept describing the ability to cope with change or disruption. Its importance in the era of emergency preparedness and response, combined with its multidisciplinary attributes, have led researches to study similarities and differences in the meaning of resilience across various fields. A systematic literature review, conducted in the field of resilience management by the DARWIN project, yielded a scientific corpus of 419 articles. In the present study, automated text-analysis approaches were used to investigate this corpus and generate insights, aiming at understanding resilience management. Three complementary computational analyses were employed: (a) topic modeling to understand the different topics or fields discussed in the articles; (b) concept maps to provide a synthetic view of key concepts in the domain and their relations; (c) psycho-linguistic analysis to identify significant psychological categories addressed in the corpus. The topic model identified four key topics: Environmental/Socioecological aspects, Organiza-tional/Operational aspects, Health, and Infrastructure/Resource Management. The concept map recognized concepts at a finer granularity level and depicted them into five main clusters with relations between them, reflecting key dimensions leading to resilience management. The psycho-linguistic analysis highlighted the importance of psychological processes within resilience management. This study identified important aspects that need to be addressed when designing resilience management frameworks, such as rehabilitation period and the role of public.
KW - Concept maps
KW - LIWC
KW - NLP tools
KW - Resilience
KW - Resilience management
KW - Topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85088376127&partnerID=8YFLogxK
U2 - 10.3808/jei.201900423
DO - 10.3808/jei.201900423
M3 - Article
AN - SCOPUS:85088376127
VL - 36
SP - 24
EP - 32
JO - Journal of Environmental Informatics
JF - Journal of Environmental Informatics
SN - 1726-2135
IS - 1
ER -