Sub-event detection from tweets

Social media plays an important role in communication between people in recent times. This includes information about news and events that are currently happening. Most of the research on event detection concentrates on identifying events from social media information. These models assume an event to be a single entity and treat it as such during the detection process. This assumption ignores that the composition of an event changes as new information is made available on social media. To capture the change in information over time, we extend an already existing Event Detection at Onset algorithm to study the evolution of an event over time. We introduce the concept of an event life cycle model that tracks various key events in the evolution of an event. The proposed unsupervised sub-event detection method uses a threshold-based approach to identify relationships between sub-events over time. These related events are mapped to an event life cycle to identify sub-events. We evaluate the proposed sub-event detection approach on a large-scale Twitter corpus.