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Disaster in the Headlines: Quantifying Narrative Variation in Global News Using Topic Modeling and Statistical Inference

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  • Fahim Sufi

    (COEUS Institute, New Market, VA 22844, USA)

  • Musleh Alsulami

    (Department of Software Engineering, College of Computing, Umm Al-Qura University, Makkah 21961, Saudi Arabia)

Abstract

Understanding how disasters are framed in news media is critical to unpacking the socio-political dynamics of crisis communication. However, empirical research on narrative variation across disaster types and geographies remains limited. This study addresses that gap by examining whether media outlets adopt distinct narrative structures based on disaster type and country. We curated a large-scale dataset of 20,756 disaster-related news articles, spanning from September 2023 to May 2025, aggregated from 471 distinct global news portals using automated web scraping, RSS feeds, and public APIs. The unstructured news titles were transformed into structured representations using GPT-3.5 Turbo and subjected to unsupervised topic modeling using Latent Dirichlet Allocation (LDA). Five dominant latent narrative topics were extracted, each characterized by semantically coherent keyword clusters (e.g., “wildfire”, “earthquake”, “flood”, “hurricane”). To empirically evaluate our hypotheses, we conducted chi-square tests of independence. Results demonstrated a statistically significant association between disaster type and narrative frame ( χ 2 = 25 , 280.78 , p < 0.001), as well as between country and narrative frame ( χ 2 = 23 , 564.62 , p < 0.001). Visualizations confirmed consistent topic–disaster and topic–country pairings, such as “earthquake” narratives dominating in Japan and Myanmar and “hurricane” narratives in the USA. The findings reveal that disaster narratives vary by event type and geopolitical context, supported by a mathematically robust, scalable, data-driven method for analyzing media framing of global crises.

Suggested Citation

  • Fahim Sufi & Musleh Alsulami, 2025. "Disaster in the Headlines: Quantifying Narrative Variation in Global News Using Topic Modeling and Statistical Inference," Mathematics, MDPI, vol. 13(13), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2049-:d:1683757
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    References listed on IDEAS

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    1. Li, Lifang & Zhang, Qingpeng & Tian, Jun & Wang, Haolin, 2018. "Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake," International Journal of Information Management, Elsevier, vol. 38(1), pages 34-41.
    2. Stefano DellaVigna & Ethan Kaplan, 2007. "The Fox News Effect: Media Bias and Voting," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1187-1234.
    3. 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.
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