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Extracting information about emerging events in large study areas through spatiotemporal and textual anal-ysis of geotagged tweets provides the possibility of moni-toring the current state of a disaster.This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas.It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data.To precisely calculate the textual similarity,three state-of-the-art text embedding methods of Word2vec,GloVe,and FastText were used to capture both syntactic and semantic similarities.The impact of selected embedding algorithms on the quality of the outputs was studied.Different com-binations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes.The proposed method was applied to a case study related to 2018 Hurricane Florence.The method was able to precisely identify events of varied sizes and densities before,during,and after the hurricane.The fea-sibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed.The proposed method was also compared to an imple-mentation based on the standard density-based spatial clustering of applications with noise algorithm,where it showed more promising results.