Citation

Khusna NI, Sumarmi, Bachri S, Astina IK, Susilo S, Idris. Heliyon 2023; 9(9): e19669.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.heliyon.2023.e19669

PMID

37809759

PMCID

PMC10558954

Abstract

Disasters have various causes, disaster management efforts, and actors involved. A systematic big data analysis is needed to identify social resilience to determine the quality of the country's resilience on disasters. This study aims to (1) determine perceptions about the causes of disasters and (2) understand perceptions of disaster management efforts. (3) identify actors involved in disasters. (4) analyze the relationship between social resilience and disaster resilience using large data sources. (5) formulate a disaster management. The research was conducted by describing in detail from the opinions of the twitter user community about disasters using the text mining method. The data retrieval and analysis process was carried out using Computer-Assisted Qualitative Data Analysis Software (CAQDAS) with MAXQDA series 2020, Gephi version 0.10.0 and SWOT analysis. The results of the study show: (1) Most of the perceptions of the causes of disasters are associated with religion; (2) Most of the perceptions about disaster management efforts are based on the application of disaster management at the recovery stage; and (3) The actors who are most involved in disaster management efforts are the security forces countries. (4) There is a strong relationship between social resilience and disaster resilience, as shown by each actor having a role in disaster management efforts. (5) There are nine formulations of development strategies in disaster management efforts. The limitation of this research is that it only uses big data from Twitter and social media sources. The implications of this research can be used as a reference for governments, organizations, communities, or others involved in disaster management efforts, especially in countries that have diversity and are prone to disasters.

Language: en

Keywords

Disaster; Twitter; Big data; Resilience; Social