A Narrative Review on Big Data and Social Media Behaviour Analysis for Crisis Response in Thailand During COVID-19 and Flooding Events
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Abstract
Social media platforms have evolved into critical real-time information hubs during crisis events, generating massive user-generated content that presents both opportunities and challenges for emergency management. Thailand's experience with the COVID-19 pandemic and recurring flood disasters provides valuable insights into leveraging social media big data for crisis response in developing countries. This narrative review synthesizes existing literature on technological frameworks, analytical methods, and practical implementations through comprehensive analysis of published studies and documented case studies. By examining distributed computing platforms, natural language processing, sentiment analysis, and geospatial mapping, this review assesses how Thailand has utilised user-generated content for emergency management. The findings reveal both technological progress and persistent systemic constraints. While initiatives such as the Anti-Fake News Centre demonstrate effective misinformation detection within two hours, significant gaps remain in five key areas, including technological infrastructure fragmentation among 48 disaster management agencies, analytical limitations in Thai-language processing, governance framework deficiencies, stakeholder coordination constraints, and digital inclusivity challenges that exclude vulnerable populations. Despite technological implementations, critical barriers include 96% failure rates in monitoring equipment and limited real-time data integration. The analysis provides a systematic examination of implementation gaps spanning technological, analytical, governance, stakeholder coordination, and inclusivity dimensions while identifying strategic opportunities, including enhanced data quality frameworks, cloud-based scalability solutions, and explainable AI integration, to strengthen Thailand's digital crisis management capabilities.
Manuscript received: 10 Jun 2025 | Revised: 7 Aug 2025 | Accepted: 24 Sep 2025 | Published: 31 Mar 2026
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