Abstract:
We use Twitter as a social sensor to help measure cross-region information connectedness and construct an information network for social learning. Using Hurricane Ida as a case study, we construct a weighted and directed climate disaster information network described by an information network adjacency matrix. We further analyze the relative importance of disaster-specific factors, demographics, and pre-existing social networks in explaining the climate disaster information generation, diffusion, and network structure. We also compare the disaster information network structure to several social network structures including friendship, mobility, and migration. Our analytical framework can be generalized to information diffusion on other topics, at different geographic scales, and using other social sensors. The modeled disaster information network can contribute to the development of effective disaster management strategies informed by real-time data. The information network structure can be linked to behavioral responses to understand social learning in a human network.
