Title: Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams,
Abstract: The local event detection is to use posting messages with geotags on social
networks to reveal the related ongoing events and their locations. Recent
studies have demonstrated that the geo-tagged tweet stream serves as an
unprecedentedly valuable source for local event detection. Nevertheless, how to
effectively extract local events from large geo-tagged tweet streams in real
time remains challenging. A robust and efficient cloud-based real-time local
event detection software system would benefit various aspects in the real-life
society, from shopping recommendation for customer service providers to
disaster alarming for emergency departments. We use the preliminary research
GeoBurst as a starting point, which proposed a novel method to detect local
events. GeoBurst+ leverages a novel cross-modal authority measure to identify
several pivots in the query window. Such pivots reveal different geo-topical
activities and naturally attract related tweets to form candidate events. It
further summarises the continuous stream and compares the candidates against
the historical summaries to pinpoint truly interesting local events. We mainly
implement a website demonstration system Event-Radar with an improved algorithm
to show the real-time local events online for public interests. Better still,
as the query window shifts, our method can update the event list with little
time cost, thus achieving continuous monitoring of the stream.