Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs

Abstract

We propose a system for detecting local events in the real-world using geolocation information from microblog documents. A local event happens when people with a common purpose gather at the same time and place. To detect such an event, we identify a group of Twitter documents describing the same theme that were generated within a short time and a small geographic area. Timestamps and geotags are useful for finding such documents, but only 0.7% of documents are geotagged and not sufficient for this purpose. Therefore, we propose an automatic geotagging method that identifies the location of non-geotagged documents. Our geotagging method successfully increased the number of geographic groups by about 115 times. For each group of documents, we extract co-occurring terms to identify its theme and determine whether it is about an event. We subjectively evaluated the precision of our detected local events and found that it had 25.5% accuracy. These results demonstrate that our system can detect local events that are difficult to identify using existing event detection methods. A user can interactively specify the size of a desired event by manipulating the parameters of date, area size, and the minimum number of Twitter users associated with the location. Our system allows users to enjoy the novel experience of finding a local event happening near their current location in real time.