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. It ranks the events by importance and iteratively decides whether to aggregate or disaggregate unexpected differences in a spatiotemporal lattice. The output is a ranked list of events that are defined by a list of matching tweets posted within a constrained period of time and location. 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.