Deep-Eware: spatio-temporal social event detection using a hybrid learning model

Abstract

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.

Keywords

• Social Data Mining
• Event Classification
• NLP
• Deep Learning
• Spatio-Temporal Scope
• Stream Data Management