Leveraging Public Sentiment for Resource Coordination in Disaster Response: A Multiagent Framework

Abstract

Crises such as natural disasters, misinformation-driven social panic, and economic disruptions place communities under immense stress, demanding rapid and adaptive response strategies. Traditional disaster management has often focused on operational logistics—such as resource allocation and task prioritization—while overlooking how evolving public sentiment and misinformation dynamics can reshape crisis outcomes. In this work, we present MiSC, a multiagent framework that unifies real-time sentiment modeling with multiagent reinforcement learning to contain misinformation and coordinate resources more effectively. By continuously tracking the spread of false narratives and gauging shifts in public sentiment, MiSC adapts counter-messaging campaigns and optimizes deployment decisions in real time. Through simulation-based evaluation, we demonstrate that this synergy between opinion modeling and adaptive decision-making yields significant gains over baseline methods, including faster sentiment recovery, enhanced misinformation control, and improved resource efficiency. By advancing scalable, interoperable AI systems that integrate social signal interpretation with crisis logistics, MiSC underscores the potential of AI-driven resilience for safeguarding communities against multifaceted and unpredictable challenges.