A Machine Learning Approach to Hydrological Event Detection from News-Informed Social Media Alerts

Participatory citizenship plays a critical role in strengthening climate change resilience, particularly in the context of natural disasters such as floods and other hydrological extremes. Citizen-generated data shared through social media platforms offer valuable real-time insights that can complement traditional environmental monitoring systems. This study proposes a machine learning-based framework to analyze multilingual news data and global X (formerly known as Twitter) data that can complement street level sensor data for improved detection and understanding of extreme hydrological events: floods and landslides. The approach identifies and filters tweets related to hazards such as floods and contextualizes them with information extracted from news reports to enhance event characterization. In addition, sentiment and emotion analysis are applied to assess public reactions and perceived event intensity. By integrating physical event signals with societal responses, the method provides a broader perspective on disaster impacts and the effectiveness of emergency responses. The results highlight the potential of combining social media analytics and machine learning to support hydrological monitoring, enhance situational awareness, and contribute to more responsive disaster management strategies in the face of increasing climate-related risks.