Khusna Nur Isroatul, Bachri Syamsul, Astina I Komang, Susilo Singgih
Dept. of Geography, Faculty of Social Science, Universitas Negeri Malang, Indonesia.
Social Science Education Study Program, Faculty of Tarbiyah and Teacher Training, Universitas Islam Negeri, Sayyid Ali Rahmatullah, Tulungagung, Indonesia.
Heliyon. 2023 Aug 30;9(9):e19669. doi: 10.1016/j.heliyon.2023.e19669. eCollection 2023 Sep.
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.
灾害有多种成因、灾害管理措施以及涉及的行为主体。需要进行系统的大数据分析以确定社会复原力，从而判定该国在灾害方面的复原力质量。本研究旨在：（1）确定对灾害成因的看法；（2）了解对灾害管理措施的看法；（3）识别参与灾害的行为主体；（4）利用大数据源分析社会复原力与灾害复原力之间的关系；（5）制定灾害管理方案。该研究通过使用文本挖掘方法，详细描述推特用户群体对灾害的看法来进行。数据检索和分析过程使用了计算机辅助定性数据分析软件（CAQDAS），包括2020版MAXQDA系列、0.10.0版Gephi以及SWOT分析。研究结果表明：（1）对灾害成因的看法大多与宗教相关；（2）对灾害管理措施的看法大多基于恢复阶段的灾害管理应用；（3）参与灾害管理措施的行为主体中，国家安保力量最为突出；（4）社会复原力与灾害复原力之间存在紧密关系，各行为主体在灾害管理措施中都发挥着作用体现了这一点；（5）灾害管理措施中有九种发展战略方案。本研究的局限性在于仅使用了来自推特和社交媒体源的大数据。本研究的意义可作为政府、组织、社区或其他参与灾害管理措施的各方的参考，尤其是在具有多样性且易受灾的国家。
