Spatio-temporal event discovery in the big social data era

Social networks have been transforming the way people express opinions, post and react to events, and share ideas. Over the last decade, several 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, these works do not consider the continuous processing of events over the social data streams, and therefore, cannot determine the spatial and temporal evolution of such events. This paper introduces a big data platform for event discovery, while tracking their evolution over space and time. We propose a scalable and efficient architecture that can manage and mine a huge data flow of unstructured streams, in order to detect geo-social events. The extracted clusters of events are indexed by a spatio-temporal index structure. We conduct experiments over twitter datasets to measure the effectiveness and efficiency of our system with respect to the existing major event detection techniques. An initial demonstration of our platform highlights its major advantage for detecting and tracking events spatially and temporally, thus allowing for great opportunities from application perspectives.