Event-Radar: Real-time Local Event Detection System for Geo-Tagged Tweet Streams

TL;DR

Event-Radar is a real-time, cloud-based system that detects local events from geo-tagged tweets by leveraging a novel cross-modal authority measure, enabling continuous monitoring and timely updates of local events.

Contribution

It introduces an improved algorithm based on GeoBurst+ for real-time local event detection from geo-tagged tweet streams, with a practical website demonstration system.

Findings

Effective real-time detection of local events from tweet streams

Continuous updating of event list with minimal delay

Demonstrated system for public use and monitoring

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.