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

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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)

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.

Suggested Citation

  • Fahim Sufi & Musleh Alsulami, 2025. "Unmasking Media Bias, Economic Resilience, and the Hidden Patterns of Global Catastrophes," Sustainability, MDPI, vol. 17(9), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:3951-:d:1644378
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    References listed on IDEAS

    as
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