This study presents a geospatial framework that combines NLP, machine learning, and GIScience to extract and georeference tweets related to the November 2016 Haifa wildfire, enabling near real-time insights into urban fire dynamics. Using OpenStreetMap and GeoNames to geocode over 16,000 tweets, the researchers demonstrated strong spatial and temporal alignment with official fire incident reports, highlighting social media’s potential as a supplementary data source for disaster response. The approach offers a scalable model for leveraging crowdsourced and user-generated data in emergency informatics, especially in data-scarce regions.