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

• URL: http://arxiv.org/abs/2605.11348v1
• Date: Tue, 12 May 2026 00:07:45 GMT
• Title: Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence
• Abstract summary: We examine whether Large Language Models (LLMs) can effectively extract causal relations from disaster-related social media posts.<n>Our findings highlight both the potential and risks of using LLMs for causal relation extraction in disaster decision-support systems.
• Score: 10.83315119677514
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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