Assessing Climate Hazard Resilience Through AI-Based Analysis of Online Data: Empirical Evidence from Galicia

Erokhin, D. ORCID: https://orcid.org/0000-0002-5191-0579 & Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179 (2026). Assessing Climate Hazard Resilience Through AI-Based Analysis of Online Data: Empirical Evidence from Galicia. Societies 16 (6) e188. 10.3390/soc16060188.

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Abstract

Climate hazards increasingly unfold as information crises alongside physical impacts, producing rapid shifts in what people search for and discuss online. This case study demonstrates how AI-supported analysis of online data can complement conventional disaster intelligence by providing a scalable social sensing layer for climate hazard resilience in Galicia. It integrates Google Trends as a proxy for changing public attention and information demand, and YouTube videos and comment threads to capture public sensemaking and resilience-relevant signals. Monthly Google Trends series were used for eight hazards, with floods showing the highest mean interest, followed by wildfires and heatwaves. For the three highest-salience hazards, the study analyzed YouTube comments using gpt-5-mini to extract sentiment, emotions, topics, institutional trust cues, collective efficacy cues, calls to action, impacts, vulnerable groups, and coping actions. The corpus included 184 heatwave comments, 20,427 wildfire comments, and 4882 flood comments. Across hazards, discourse is predominantly negative but differs in structure. Heatwave threads skew toward mockery and normalization, wildfire threads center on anger, governance and low institutional trust, and flood threads combine solidarity with demands for localized warnings and guidance. The study translates comment-level signals into traceable policy recommendations emphasizing actionable risk communication, early warning and response capacity, and trust-building practices. The study concludes with an operational pipeline concept for continuous monitoring and dashboard-based decision support, while emphasizing limitations related to Google Trends sampling and normalization, platform and API biases, and model-mediated uncertainty.

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