Unveiling Dynamics in Climate Disaster Information Transmission Across Regions: A Variance Decomposition Network Approach

Abstract

Modeling the dynamic transmission of climate disaster information during these events is vital for effective disaster preparation, response, and recovery. This study uses Hurricane Ida as a case study and Twitter (currently branded as X) as a social sensor to construct a disaster information network that captures climate disaster information transmission across regions. Analyzing the co-movement of region-specific hurricane-related tweet trends that reflect unobservable disaster information in each region, we decompose the forecast error variance and covariance matrix to develop a weighted and directed information network adjacency matrix. We identify 79 sub-state regions with high flood risk but low hurricane information indegree. While most disaster information exchange occurs within each region, cross-region exchange closely aligns with snapshots of the Facebook friendship, SafeGraph mobility, and pre-existing migration networks. Notably, it diverges from the baseline Twitter communication network, highlighting the vital role of social-emotional connections in disseminating disaster information compared to other types of information. Disaster location and local population size predict disaster information indegree and outdegree, and the pre-existing migration network mediates the spread of disaster information. The disaster information network exhibits a higher proportion of unexplained variations compared to other social networks, underscoring the importance of collecting supplementary real-time data through social sensing.

Sébastien Box-Couillard
Sébastien Box-Couillard
Ph.D. Candidate in Applied Economics

I am a Ph.D. candidate in Agricultural and Consumer Economics at the University of Illinois Urbana-Champaign. My research interests lie at the intersection of environmental and urban economics. I am particularily interested in interactions between inequality, discrimination, housing markets and natural disasters.

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