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. To this end, we (1) propose an expert-grounded evaluation framework that compares LLM-generated causal graphs with reference graphs derived from disaster-specific reports and (2) assess whether the extracted relations are supported by post-event evidence or instead reflect model priors. Our findings highlight both the potential and risks of using LLMs for causal relation extraction in disaster decision-support systems.
@article{arxiv.2605.11348,
title = {Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence},
author = {Ujun Jeong and Saketh Vishnubhatla and Bohan Jiang and Andre Harrison and Adrienne Raglin and Huan Liu},
journal= {arXiv preprint arXiv:2605.11348},
year = {2026}
}