Review article: Social media for managing disasters triggered by natural hazards: a critical review of data collection strategies and actionable insights

Abstract. This paper presents a comprehensive critical review of 250 studies published between January 2010 and September 2023 that examine how social media data have been used to manage disasters triggered by natural hazards. The review focuses on data collection, processing, and analysis strategies, and evaluates their effectiveness in transforming social media content into actionable information for disaster preparedness, response, and recovery. A Social Media Literature Database (SMLD) was developed to support this analysis, categorising each study into seven main categories and 27 subcategories covering (a) article details, (b) case study regions, (c) disaster events, (d) social media platforms, (e) data characteristics, (f) collection and analysis methods, and (g) evaluation approaches. The reviewed literature encompasses disasters resulting from a wide range of natural hazards, most frequently floods, hurricanes, and earthquakes, but also including storms, wildfires, volcanic eruptions, landslides, droughts, and multi-hazard events. To assess how effectively social media contributes to actionable disaster information, the studies were further classified into nine thematic areas, including (a) public discourse and sentiment analysis, (b) temporal and spatial insights, (c) relevance filtering, (d) community and stakeholder engagement, (e) disaster trend identification, and (f) resource mapping. While Twitter (X) dominated as the primary data source, other platforms such as Facebook, Instagram, Weibo, and Reddit were also employed for text, image, and video analyses. Natural Language Processing methods, particularly content analysis, were widely used for relevance filtering and noise reduction, while Machine Learning approaches such as Support Vector Machines, Naive Bayes, and Neural Networks supported classification and event detection. Temporal and spatial analyses were common, though their effectiveness in filtering relevant data varied. The categorisation of actionable information reveals continuing research gaps in understanding community interactions, cross-platform data integration, and resource identification during and after disasters. Drawing on the reviewed studies and the authors' own experience, six best practices are proposed for community use of social media during disasters and five for researchers seeking to enhance the integration of social media analytics into disaster management and resilience strategies.