Social media platforms provide a real-time lens into public sentiment during natural disasters; however, models built solely on textual data often reinforce urban-centric biases and overlook underrepresented communities. This paper introduces an adaptive cross-city learning framework that enhances disaster sentiment understanding by integrating mobility-informed behavioral signals and city similarity-based data augmentation. Focusing on the January 2025 Southern California wildfires, our model achieves state-of-the-art performance and reveals geographically diverse sentiment patterns, particularly in areas experiencing overlapping fire exposure or delayed emergency responses. We further identify positive correlations between emotional expressions and real-world mobility shifts, underscoring the value of combining behavioral and textual features. Through extensive experiments, we demonstrate that multimodal fusion and city-aware training significantly improve both accuracy and fairness. Collectively, these findings highlight the importance of context-sensitive sentiment modeling and provide actionable insights toward developing more inclusive and equitable disaster response systems.
A Unified Model for Human Mobility Generation in Natural Disasters
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（电子工程系，信息科学与技术国家研究院（BNRist），清华大学）；Department of Civil and Environmental Engineering, Northeastern University（土木与环境工程系，东北大学）
A Transformer-Based Cross-Platform Analysis of Public Discourse on the 15-Minute City Paradigm
1 College of Science；Engineering, Texas State University, San Marcos, Texas, USA Email；3 Texas A\&M Transportation Institute, Texas A\&M University, Houston, Texas, USA Email
Identifying Diversity, Equity, Inclusion, and Accessibility (DEIA) Indicators for Transportation Systems using Social Media Data: The Case of New York City during Covid-19 Pandemic
