Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence

ArXi:2605.11348v1 Announce Type: cross During disasters, extracting causal relations from social media can strengthen situational awareness by identifying factors linked to casualties, physical damage, infrastructure disruption, and cascading impacts. However, disaster-related posts are often informal, fragmented, and context-dependent, and they may describe personal experiences rather than explicit causal relations. In this work, we examine whether Large Language Models (LLMs) can effectively extract causal relations from disaster-related social media posts.