Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data

2026-06-08

AI Summary

Existing research often examines human mobility and social media sentiment during crises in isolation, lacking an integrated cross-domain framework. This study proposes the first interpretable multimodal fusion approach that establishes a structured translation mechanism from data to policy by combining behavioral state binarization, Formal Concept Analysis (FCA), and temporal-preserving association rule mining. Evaluated on case studies of the Los Angeles wildfires and the UAE pandemic response, the method uncovers behavior rules with 100% confidence (lift = 2.5) in the wildfire context, along with eight same-day stable rules and forty cross-domain patterns exhibiting predictive power over 2–7 days in the pandemic scenario. These findings demonstrate both the scientific validity and practical policy relevance of the proposed framework.

Abstract

Crises alter both how people move and how they communicate. During emergencies such as wildfires and pandemics, changes in mobility patterns and online emotional discourse evolve jointly, yet they are typically studied in isolation. This paper presents a unified and interpretable pipeline that integrates mobility and social media data to identify cross-domain behavioral patterns in crisis settings. The framework is evaluated through two case studies: a short-horizon analysis of the January 2025 Los Angeles wildfires (prototype case) and a longitudinal analysis of UAE COVID-19 behavior from March 2020 to December 2021 (primary case, 671 days). The pipeline aligns heterogeneous daily signals, transforms them into binary behavioral states, applies Formal Concept Analysis (FCA) to extract co-occurrence structure, mines association rules, and validates rule stability through chronological holdout testing. A structured policy-translation layer renders robust rules as operational briefs specifying triggers, lead times, and action playbooks. Results reveal clear cross-domain behavioral structure in both crises. In the wildfire case, traffic stress, fear/anger sentiment, and governance discourse are tightly coupled within a 33-day window, with key rules reaching 100\% confidence and lift scores up to 2.5. In the COVID case, repeated mobility adaptation and sentiment volatility yield 8 stable same-day rules (88\% holdout pass rate) and 40 clean predictive rules with 2--7 day lead horizons. The work demonstrates that interpretable multimodal fusion can produce both scientifically credible and policy-actionable crisis intelligence.

Problem

• crisis behavior
• mobility data
• social media data
• cross-domain patterns
• interpretable analysis

Innovation

• interpretable multimodal fusion
• Formal Concept Analysis
• cross-domain behavioral patterns
• association rule mining
• policy-actionable intelligence