Framing impact, shaping response: Linking affectedness and action in humanitarian practice

Understanding how disasters impact communities and how humanitarian organisations respond is essential for improving disaster preparedness, response, and policy. However, humanitarian organizations, government agencies and scientific institutions often report on disaster impacts and response in unstructured narrative reports, limiting its accessibility for systematic analysis. In this study, we developed a data-driven pipeline to extract and classify impact and response information from the International Federation of Red Cross and Red Crescent Societies (IFRC) disaster appeals and operational reports. We processed the text into clean sentences and manually annotated a stratified set of reports, covering different climate hazard types. Sentences were labelled as reporting impacts, reporting response measures, or neither, and those describing impacts or responses were further categorised into a taxonomy of 24 impact subclasses and 26 response subclasses. Annotations were used to train four text classification models for detecting and classifying impact- and response-related sentences. Our approach demonstrates the feasibility of automatically extracting structured disaster impact and response data from humanitarian narrative reports, enabling large-scale analytics and supporting evidence-based disaster management.