DisImpact: Quantifying the Physi-Social Impact of Natural Disasters Through Social Media

2026-05-19

AI Summary

Existing approaches struggle to quantify the physical damage of natural disasters and their cascading societal effects in a timely and comprehensive manner. This study proposes DisImpact, a novel framework that integrates multi-platform, multimodal social media data and leverages multimodal large language models to enable fine-grained classification of disaster impacts. The framework establishes ten distinct indicators spanning both physical and social dimensions and synthesizes them with public engagement intensity to generate a unified, comparable, and aggregatable dynamic disaster impact index. Validation against FEMA assistance records and NASA fire hotspot data demonstrates significant correlation. Analyses reveal that physical impacts are spatially concentrated within affected areas during the event, whereas societal impacts exhibit temporal lag and broader geographic diffusion.

Abstract

Natural disasters not only cause large-scale physical destruction, but also cascading social consequences that are difficult to quantify with traditional surveys and reports. Social media platforms offer an alternative perspective that captures multimodal, real-time, and user-generated content that can be leveraged for disaster impacts. In this paper, we introduce DisImpact, a two-stage framework that systematically quantifies the physi-social impacts of disasters via a Multimodal Large Language Model (MLLM). The social media posts are first classified into ten disaster impact categories that cover both physical and social domains. We then construct a disaster impact index that integrates the relative prominence of each category with the intensity of public engagement on a weekly basis. This design provides a unified scale for representing disaster impacts across both individual disaster impact categories and the broader physical and social domains. The unified representation enables direct comparison across categories and allows the impacts to be flexibly aggregated to reveal higher-level patterns and overall trends. We validate the impact indices against authoritative ground-truth data, including FEMA Public Assistance data and NASA FIRMS fire detections, observing consistent lead-lag correlations that demonstrate strong validity across both social and physical impact dimensions. We further conduct temporal and spatial analyses, and the results show that physical impacts are often peak during the disasters and localized in regions that are directly affected by disasters, while social impacts often emerge later and spread more broadly across time and space. To the best of our knowledge, this is the first framework to comprehensively quantify disaster impacts across their physical and social dimensions using multimodal data from multiple social media platforms.

Problem

• natural disasters
• physi-social impact
• social media
• impact quantification
• multimodal data

Innovation

• Multimodal Large Language Model
• Disaster Impact Quantification
• Social Media Analysis
• Physi-Social Impact
• Disaster Impact Index