Background The 2025 ice-rock avalanche that destroyed the Swiss Alpine village of Blatten in May 2025 represents an important example of high-altitude cascading risk. It also received wide media attention. This study analyses how the Blatten disaster was represented in Swiss news media, examining descriptions of the hazard cascade, disaster response strategies, dominant themes, and broader media frames. Using a corpus of 2,052 news articles, the study combines structured large language model extraction with human validation, topic modelling, and framing synthesis to analyse multilingual media coverage at scale. Results Swiss media consistently portrayed Blatten as a cascading high-mountain disaster involving rock slope failure, glacier collapse, debris flows, river damming, lake formation, and downstream flood risk. Coverage highlighted a broad portfolio of disaster risk management measures, including monitoring, early warning, evacuation, emergency interventions, financial assistance, insurance, reconstruction, and possible relocation. Topic modelling revealed that reporting extended well beyond the physical hazard to include community disruption, governance, funding, tourism, insurance, and long-term recovery. Framing analysis identified competing narratives in which successful emergency management coexisted with debates over reconstruction versus retreat, cultural identity, financial responsibility, and the future habitability of Alpine communities. Climate change emerged as an overarching but contested frame, appearing both as a driver of increasing mountain hazards and as the subject of scientific, political, and societal debate. Conclusions The Blatten disaster was represented not only as a cascading natural hazard but also as a catalyst for broader debates on climate change adaptation and the future of mountain communities. The findings demonstrate that media discourse extends beyond physical processes and emergency response to encompass governance, finance, cultural values, and place attachment. Integrating computational text analysis with qualitative framing provides a scalable approach for examining societal interpretations of disasters and can support interdisciplinary research on climate risks, disaster risk management, and long-term adaptation.
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