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

Fahim Sufi () and Musleh Alsulami
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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

Mathematics, 2025, vol. 13, issue 13, 1-23

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.

Keywords: media bias; AI-driven crisis management; disaster narratives; topic modeling; crisis communication; open-source disaster data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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