Event Detection on Twitter by Mapping Unexpected Changes in Streaming Data into a Spatiotemporal Lattice

Many applications seek to make sense of high volume streaming data from social media by identifying spatiotemporal patterns. Events, representing topics that emerge and decay over time, are detected by monitoring for changes in the language being used, but typical approaches do not consider the localisation of events in cities and countries, and within hours, days, and weeks. This work develops and evaluates a new approach to event localisation and ranking that can be applied to Twitter data streams. The proposed approach models the use of language in tweets per city per hour to produce a model that can be used to detect the magnitude of unexpected changes in the use of the language. The approach uses a spatiotemporal lattice structure and a method for traversing between hours, days, and weeks, as well as cities, regions, and countries to identify anomalies in the language used across millions of tweets. The output is a ranked list of events comprising a list of tweets posted within a location and period of time, and characterized by language features of interest. The approach was implemented and tested by comparing events detected across five example domains (suicide, shooting, elections, sports, and sentiment) using 11.7 million tweets from users located in 100 cities and posted within the 203-day study period. Experiments demonstrate that the approach can detect events across a range of application domains.