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Twitter updates now represent an enormous stream of information originatingfrom a wide variety of formal and informal sources, much of which is relevantto real-world events. In this paper we adapt existing bio-surveillancealgorithms to detect localised spikes in Twitter activity corresponding to realevents with a high level of confidence. We then develop a methodology toautomatically summarise these events, both by providing the tweets which fullydescribe the event and by linking to highly relevant news articles. We applyour methods to outbreaks of illness and events strongly affecting sentiment. Inboth case studies we are able to detect events verifiable by third partysources and produce high quality summaries.

The Early Bird Catches The Term: Combining Twitter and News Data For Event Detection and Situational Awareness