Unmasking Media Bias, Economic Resilience, and the Hidden Patterns of Global Catastrophes

Fahim Sufi () and Musleh Alsulami
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Fahim Sufi: School of Public Health and Preventive Medicine, Monash University, Australia, VIC 3004, Australia
Musleh Alsulami: Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21961, Saudi Arabia

Sustainability, 2025, vol. 17, issue 9, 1-25

Abstract: The increasing frequency and destructiveness of natural disasters necessitate scalable, transparent, and timely analytical frameworks for risk reduction. Traditional disaster datasets—curated by intergovernmental bodies such as EM-DAT and UNDRR—face limitations in spatial granularity, temporal responsiveness, and accessibility. This study addresses these limitations by introducing a novel, AI-enhanced disaster intelligence framework that leverages 19,130 publicly available news articles from 453 global sources between September 2023 and March 2025. Using OpenAI’s GPT-3.5 Turbo model for disaster classification and metadata extraction, the framework transforms unstructured news text into structured variables across five key dimensions: severity, location, media coverage, economic resilience, and casualties. Hypotheses were tested using statistical modeling, geospatial aggregation, and time series analysis. Findings confirm a modest but significant correlation between severity and casualties ( ρ = 0.12 , p < 10 − 60 ), and a stronger spatial correlation between average regional severity and impact ( ρ = 0.31 , p < 10 − 10 ). Media amplification bias was empirically demonstrated: hurricanes received the most coverage (5599 articles), while under-reported earthquakes accounted for over 3 million deaths. Economic resilience showed a statistically significant but weak protective effect on fatalities ( β = − 0.024 , p = 0.041 ). Disaster frequency increased substantially over time (slope η 1 = 53.17 , R 2 = 0.32 ), though severity remained stable. GPT-based classification achieved a high average F1-score (0.91), demonstrating robust semantic accuracy, though not mortality prediction. This study validates the feasibility of using AI-curated, open-access news data for empirical hypothesis testing in disaster science, offering a sustainable alternative to closed datasets and enabling real-time policy feedback loops, particularly for vulnerable, data-scarce regions.

Keywords: sustainable disaster risk reduction; AI for climate resilience; geospatial intelligence for sustainability; media bias and environmental justice; AI-driven crisis management; predictive modeling for sustainable policy; economic resilience and social sustainability; open-source disaster data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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