SIDE: Socially Informed Drought Estimation Toward Understanding Societal Impact Dynamics of Environmental Crisis
Lanyu Shang, Bozhang Chen, Shiwei Liu, Yang Zhang, Ruohan Zong, Anav, Vora, Ximing Cai, Na Wei, Dong Wang

TL;DR
This paper introduces SIDE, a novel AI framework that leverages social media and news data to jointly estimate drought severity and societal impact, addressing limitations of traditional drought monitoring methods.
Contribution
The paper presents SIDE, a new socially informed AI model that explicitly captures societal impacts and social-physical interdependencies in drought estimation.
Findings
SIDE outperforms existing methods in accuracy on real-world datasets.
It effectively models the social-physical interdependence of drought impacts.
The approach provides insights for human-centric drought mitigation strategies.
Abstract
Drought has become a critical global threat with significant societal impact. Existing drought monitoring solutions primarily focus on assessing drought severity using quantitative measurements, overlooking the diverse societal impact of drought from human-centric perspectives. Motivated by the collective intelligence on social media and the computational power of AI, this paper studies a novel problem of socially informed AI-driven drought estimation that aims to leverage social and news media information to jointly estimate drought severity and its societal impact. Two technical challenges exist: 1) How to model the implicit temporal dynamics of drought societal impact. 2) How to capture the social-physical interdependence between the physical drought condition and its societal impact. To address these challenges, we develop SIDE, a socially informed AI-driven drought estimation…
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Taxonomy
TopicsClimate change impacts on agriculture
MethodsFocus
