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A Big Data Platform For Spatio-Temporal Social Event Discovery

The tremendous rise of location-enriched microblogging has made it possible to discover social events from social media, as well as their evolution over space and time. Over the last decade, multiple studies on event detection from social media have been proposed, with the aim of extracting specific types of events, such as, social gatherings, natural disasters, and emergency situations, among others. However, existing works do not consider the incremental and continuous processing of events over the large amounts of social streams, and therefore, cannot determine the spatial and temporal evolution of such events. This work presents a big data mining platform for the incremental discovery of geo-social events based on a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams. We demonstrate our early results over twitter datasets and discuss its main advantage by incorporating advanced features for event extraction, thus allowing for great opportunities from application perspectives.