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Smart city article details
| Title | Deep-Eware: Spatio-Temporal Social Event Detection Using A Hybrid Learning Model |
|---|---|
| ID_Doc | 18097 |
| Authors | Afyouni I.; Khan A.; Aghbari Z.A. |
| Year | 2022 |
| Published | Journal of Big Data, 9, 1 |
| DOI | http://dx.doi.org/10.1186/s40537-022-00636-w |
| Abstract | Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others. 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 ‘Deep-Eware’, 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’ 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. © 2022, The Author(s). |
| Author Keywords | Deep Learning; Event Classification; NLP; Social Data Mining; Spatio-Temporal Scope; Stream Data Management |
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