Metamodel‐Powered Social Media Image Processing for Decision Support in Crisis Response

ABSTRACT Social media imagery serves as a crucial data source for crisis responders to perceive the evolving crisis situations. The crisis‐related information extracted from these images can be used to enhance situational awareness and support decision‐making. However, such information provided by data‐driven methods is difficult to exploit by model‐driven systems, which are widely employed in crisis management practice. This information mismatch caused by the semantic gap undermines the value of social media images in crisis informatics. To address this problem, a metamodel‐powered framework for social media image processing is proposed to support crisis response. This framework integrates deep learning techniques, a disaster‐specific dataset, information transformation middleware, and a crisis‐oriented metamodel. By doing so, it provides ready‐for‐exploitation information, enabling crisis responders to effectively utilise social media image data. The proposed framework is demonstrated through a case study on the 2018 Aude heave precipitation event and further validated against four additional historical crises. The primary contribution lies in the development of a novel design artefact that follows the design science research paradigm. This study not only addresses the specific information mismatch issue but also offers generalizable design principles applicable to information systems facing similar challenges.