Bridging the Urban Divide: Adaptive Cross-City Learning for Disaster Sentiment Understanding

TL;DR

An adaptive cross-city learning framework that enhances disaster sentiment understanding by integrating mobility-informed behavioral signals and city similarity-based data augmentation and demonstrates that multimodal fusion and city-aware training significantly improve both accuracy and fairness.

Abstract

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.

Figures (7)

• Figure 1: The overall framework.
• Figure 2: City label distribution comparison.
• Figure 3: Estimated sentiment distribution among (a) adaptive model, (b) pure text model, and (c) LLM-based model.
• Figure 4: Effect of mobility embedding on sentiment representation via t-SNE.
• Figure 5: Correlation between sentiment score and average mobility across selected cities.