![]() The dynamic updates of such events by the live communities in social media lay the ground for developing plenty of intelligent location-based services (LBSs). ![]() Social events usually comprise family reunions, promotions, incidents, announcements, or natural hazards, among others. a new festival or a start of a shopping discount season) in the city of Dubai, a large number of tweets will be posted about such a social event. For example, when a social attraction occurs (e.g. The content generated by users is massive and rich therefore, researchers, stakeholders, and authorities can build applications to extract insightful spatio-temporal information about live events of interest (EoI).Ī ‘social event’ can be commonly defined as the occurrence within a specified space and time of a real-world unusual happening (Huang et al. People’s interests, feedback, check-ins, and events are among the hot topics discussed on daily bases over social media sites (Chauhan et al. The interaction of users through social media, such as Twitter and Flickr, has paved the way for productive insights and discoveries. Over the last two decades, social media has emerged as a great support for understanding the behavior of users and communities. This leads to the development of unparalleled smart city applications, such as event-enriched trip planning, epidemic disease evolution, and proactive emergency management services. The results demonstrate that E-ware has a major advantage for real-time incremental detection and tracking of events, both spatially and temporally. We conduct experiments over Twitter datasets to measure the effectiveness and efficiency of our system. The system integrates an efficient spatio-temporal index for fast retrieval and updates of evolving event clusters. ![]() Our incremental clustering technique employs temporal sliding windows, in order to update the discovered topic clusters with the upcoming social streams (i.e., tweets). 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. We present ‘E-ware’, a scalable and efficient big data platform that integrates data stream and geospatial processing tools for the incremental extraction and dissemination of spatio-temporal events. This paper introduces a new perspective for the incremental extraction and clustering of social events from big social data streams. Event detection from social media aims at extracting specific or generic unusual happenings, such as, family reunions, earthquakes, and disease outbreaks, among others.
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