Spatio-temporal Event Detection using Poisson Model and Quad-tree on Geotagged Social Media

Abstract

Identifying events happening in a specific locality is important as an early warning for accidents, protests, elections or breaking news. However, this location-specific event detection is challenging as the locations and types of events are not known beforehand. To address this problem, we propose an online spatiotemporal event detection system using social media that is able to detect events at different time and space resolutions. First, we exploit a quad-tree method to split the geographical space into multiscale regions based on the density of social media data. Then, we implement a statistical unsupervised approach using Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is estimated by merging events happening in the same region at consecutive time intervals. A post processing stage is introduced to filter out events that are spam, fake or wrong. Finally, we incorporate simple semantics by using social media entities to assess the integrity, and accuracy of detected events. The proposed method is evaluated using Twitter and Flickr for the city of Melbourne based on recall and precision measures. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.