title: The early bird catches the term: combining twitter and news data for event detection and situational awareness.
abstract: Twitter updates now represent an enormous stream of information originating from a wide variety of formal and informal sources, much of which is relevant to real-world events. They can therefore be highly useful for event detection and situational awareness applications.
  
  
  
  In this paper we apply customised filtering techniques to existing bio-surveillance algorithms to detect localised spikes in Twitter activity, showing that these correspond to real events with a high level of confidence. We then develop a methodology to automatically summarise these events, both by providing the tweets which best describe the event and by linking to highly relevant news articles. This news linkage is accomplished by identifying terms occurring more frequently in the event tweets than in a baseline of activity for the area concerned, and using these to search for news. We apply our methods to outbreaks of illness and events strongly affecting sentiment and are able to detect events verifiable by third party sources and produce high quality summaries.
  
  
  
  This study demonstrates linking event detection from Twitter with relevant online news to provide situational awareness. This builds on the existing studies that focus on Twitter alone, showing that integrating information from multiple online sources can produce useful analysis.
authors.affiliation: Institute for Security Science and Technology, Imperial College London, Exhibition Road, London, UK. nicholas.thapen@imperial.ac.uk.
authors.forename: Nicholas
authors.identifier: 0000-0003-0654-8213
authors.initials: N
authors.lastname: Thapen
authors.affiliation: Institute for Security Science and Technology, Imperial College London, Exhibition Road, London, UK.
authors.forename: Donal
authors.initials: D
authors.lastname: Simmie
authors.affiliation: Institute for Security Science and Technology, Imperial College London, Exhibition Road, London, UK.
authors.forename: Chris
authors.initials: C
authors.lastname: Hankin
mesh_terms: D000465:Algorithms; D062665:Epidemiological Monitoring; D006801:Humans; D033181:Information Dissemination; D009323:Natural Language Processing; D061108:Social Media
keywords: Event detection; Situational awareness; Twitter