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This paper introduces a new perspective for the hybrid extraction and clustering of social events from big social data streams. We rely on a hybrid learning model, where supervised deep learning is used for feature extraction and topic classification, whereas unsupervised spatial clustering is employed to determine the event whereabouts. We present<jats:italic>\u2018Deep-Eware\u2019<\/jats:italic>, a scalable and efficient event-aware big data platform that integrates data stream and geospatial processing tools for the hybrid extraction and dissemination of spatio-temporal events. We introduce a pure incremental approach for event discovery, by developing unsupervised machine learning and NLP algorithms and by computing events\u2019 lifetime and spatial spanning. The system integrates a semantic keyword generation tool using KeyBERT for dataset preparation. Event classification is performed using CNN and bidirectional LSTM, while hierarchical density-based spatial clustering was used for location-inference of events. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The results demonstrate that this hybrid approach for spatio-temporal event extraction has a major advantage for real-time spatio-temporal event detection and tracking from social media. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services.<\/jats:p>","DOI":"10.1186\/s40537-022-00636-w","type":"journal-article","created":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T10:16:59Z","timestamp":1656411419000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep-Eware: spatio-temporal social event detection using a hybrid learning model"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6686-3069","authenticated-orcid":false,"given":"Imad","family":"Afyouni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aamir","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaher Al","family":"Aghbari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"636_CR1","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.eswa.2016.02.028","volume":"55","author":"M Adedoyin-Olowe","year":"2016","unstructured":"Adedoyin-Olowe M, Gaber MM, Dancausa CM, Stahl F, Gomes JB. 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