From Rumors to Risk: Mapping and Modeling Climate-Disaster Misinformation (Proposals Track)
Tristan Ballard (Independent)
Abstract
Recent years have seen a surge in climate-disaster misinformation, with social media amplifying unfounded claims in the lead-up to and aftermath of major disasters. This misinformation has hindered disaster preparation and recovery while fueling harassment against meteorologists and government officials, eroding trust in scientific institutions. While tools exist for analyzing general climate-change misinformation, current datasets often overlook the rapidly shifting narratives tied to specific events like wildfires, floods, or hurricanes. This proposal addresses that gap by developing a dynamic, evolving dataset on climate-disaster misinformation. Built through targeted social media data collection and rigorous labeling, the dataset will adapt alongside AI/ML advancements through iterative feedback from model performance and emerging trends. This openly accessible resource will enable researchers and practitioners to refine detection algorithms, design interventions, and inform crisis communication strategies—ensuring both data and models remain aligned with the shifting misinformation landscape. Ultimately, this work seeks to clarify key drivers of misinformation propagation and support more effective climate disaster response.