Supporting Humanitarian Crisis Decision Making with Reliable Intelligence Derived from Social Media Using AI

Abstract Recent advances in the field of artificial intelligence bring promising new capabilities that can substantially improve our ability to manage complex and evolving situations in the face of uncertainty. Humanitarian crises exemplify such situations, and the pervasiveness of social media renders it one of the most abundant sources of real-time information available. However, it is quite a difficult task to condense a body of social media posts into useful information quickly. In this paper we consider the challenge of using social media reports to provide a reliable, real-time situational awareness in the management of humanitarian crises. Effectively addressing this challenge requires extracting only the relevant information out of text and images in individual social media posts, fusing this information together into actionable information points for decision makers, and providing an assessment of the trustworthiness of this information. We propose a general solution framework and discuss a system developed in collaboration with NATO which combines state-of-the-art deep learning, natural language processing, computer vision, and information fusion models to provide a reliable, actionable, real-time situational awareness for supporting decision making in humanitarian crisis logistics. In addition to the technical approach, we also discuss important practical aspects of this project including the development and validation process, challenges encountered along the way, and key lessons learned.