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.

翻译：危机不仅改变人们的移动方式，也改变人们的沟通方式。在野火、流行病等紧急事件中，移动模式的变化与在线情绪话语的演变是同步进行的，但这两者通常被孤立研究。本文提出了一个统一且可解释的分析框架，该框架整合移动性和社交媒体数据，以识别危机情境下的跨领域行为模式。该框架通过两个案例研究进行评估：针对2025年1月洛杉矶野火的短期分析（原型案例）和针对2020年3月至2021年12月（671天）阿联酋COVID-19行为的纵向分析（主要案例）。该分析流程对齐异质的日常信号，将其转化为二进制行为状态，应用形式概念分析（FCA）提取共现结构，挖掘关联规则，并通过时间顺序留出测试验证规则稳定性。一个结构化的政策转化层将稳健的规则呈现为可操作简报，其中指明触发条件、提前时间和行动方案。结果表明，在这两次危机中均存在清晰的跨领域行为结构。在野火案例中，交通压力、恐惧/愤怒情绪和治理话语在33天的时间窗口内紧密耦合，关键规则置信度达到100%，提升度高达2.5。在COVID案例中，反复的移动性适应和情绪波动产生了8条稳定的同天规则（88%的留出测试通过率）和40条清晰的预测性规则，提前时间窗口为2至7天。这项工作表明，可解释的多模态融合能够生成既具有科学可信度又可付诸政策行动的危机情报。