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Real-Time Detection Tracking and Monitoring of Automatically Discovered Events in Social Media (2).pdf (221.46 kB)
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Real-time detection, tracking, and monitoring of automatically discovered events in social media

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conference contribution
posted on 2014-12-09, 12:04 authored by Miles Osborne, Sean Moran, Richard McCreadie, Alexander Von Lunen, Martin SykoraMartin Sykora, Elizabeth Cano, Neil Ireson, Craig MacDonald, Iadh Ounis, Yulan He, Tom JacksonTom Jackson, Fabio Ciravegna, Ann O'Brien
We introduce ReDites, a system for realtime event detection, tracking, monitoring and visualisation. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. Events are automatically detected from the Twitter stream. Then those that are categorised as being security-relevant are tracked, geolocated, summarised and visualised for the end-user. Furthermore, the system tracks changes in emotions over events, signalling possible flashpoints or abatement. We demonstrate the capabilities of ReDites using an extended use case from the September 2013 Westgate shooting incident. Through an evaluation of system latencies, we also show that enriched events are made available for users to explore within seconds of that event occurring.


This work was funded by EPSRC grant EP/L010690/1. MO also acknowledges support from grant ERC Advanced Fellowship 249520 GRAMPLUS.



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Published in

The 52nd annual meeting of the Association for Computational Linguistics (ACL)


59 - ?


OSBORNE, M. ... et al, 2014. Real-time detection, tracking, and monitoring of automatically discovered events in social media. IN: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 23rd-24th June 2014, Baltimore, Maryland, USA. Association for Computational Linguistics, pp. 37-42.


Association for Computational Linguistics / © The Authors


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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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This conference paper was presented at The 52nd Annual Meeting of the Association for Computational Linguistics.




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