Interpretable Crisis Behavior Analysis Using Mobility and Social Media Data
2026-06-08 • Computers and Society
Computers and SocietyComputation and Language
AI summaryⓘ
The authors studied how people’s movements and emotions online change together during emergencies like wildfires and the COVID-19 pandemic. They created a method that mixes data about where people go and what they say online, then finds meaningful patterns that happen at the same time or predict each other. They tested this approach on the 2025 Los Angeles wildfires and COVID-19 in the UAE, discovering strong connections between traffic, emotions, and government talk, as well as rules that predict behavior changes days in advance. Their work shows that combining different types of data can offer useful and understandable insights for managing crises.
Mobility patternsSocial media analysisFormal Concept AnalysisAssociation rulesCrisis behaviorSentiment analysisWildfiresCOVID-19Lead time predictionMultimodal data fusion
Authors
Muhammad Hamza Arshad Majeed, Sidahmed Benabderrahmane, Talal Rahwan
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.