Real-time timeline summarisation for high-impact events in Twitter.

Abstract

Twitter has become a valuable source of event-related information, namely, breaking news and local event reports. Due to its capability of transmitting information in real-time, Twitter is further exploited for timeline summarisation of high-impact events, such as protests, accidents, natural disasters or disease outbreaks. Such summaries can serve as important event digests where users urgently need information, especially if they are directly affected by the events. In this paper, we study the problem of timeline summarisation of high-impact events that need to be generated in real-time. Our proposed approach includes four stages: classification of realworld events reporting tweets, online incremental clustering, postprocessing and sub-events summarisation. We conduct a comprehensive evaluation of different stages on the “Ebola outbreak” tweet stream, and compare our approach with several baselines, to demonstrate its effectiveness. Our approach can be applied as a replacement of a manually generated timeline and provides early alarms for disaster surveillance.