Real-time Spatio-temporal Event Detection on Geotagged Social Media

TL;DR

This paper presents an online system for real-time detection of social media events across multiple spatial and temporal scales, using a quad-tree spatial partitioning and statistical analysis to identify significant social media activity clusters.

Contribution

The work introduces a multiscale, unsupervised spatio-temporal event detection method that adapts to unknown event resolutions and incorporates semantic filtering for improved accuracy.

Findings

Effective detection of events in multiple cities using Twitter and Flickr data.

Improved precision and recall over baseline fixed-space and clustering methods.

Introduction of a new quality measure called strength index for event accuracy.

Abstract

A key challenge in mining social media data streams is to identify events which are actively discussed by a group of people in a specific local or global area. Such events are useful for early warning for accident, protest, election or breaking news. However, neither the list of events nor the resolution of both event time and space is fixed or known beforehand. In this work, we propose an online spatio-temporal event detection system using social media that is able to detect events at different time and space resolutions. First, to address the challenge related to the unknown spatial resolution of events, a quad-tree method is exploited in order to split the geographical space into multiscale regions based on the density of social media data. Then, a statistical unsupervised approach is performed that involves Poisson distribution and a smoothing method for highlighting regions with unexpected density of social posts. Further, event duration is precisely 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 different social media datasets: Twitter and Flickr for different cities: Melbourne, London, Paris and New York. To verify the effectiveness of the proposed method, we compare our results with two baseline algorithms based on fixed split of geographical space and clustering method. For performance evaluation, we manually compute recall and precision. We also propose a new quality measure named strength index, which automatically measures how accurate the reported event is.