The early bird catches the term: combining twitter and news data for event detection and situational awareness
File(s)
Published version
Author(s)
Thapen, N
Simmie, D
Hankin, CL
Type
Journal Article
Abstract
Background: 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.
Results: 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.
Conclusions: 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.
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.
Results: 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.
Conclusions: 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.
Date Issued
2016-10-07
Date Acceptance
2016-09-20
Citation
Journal of Biomedical Semantics, 2016, 7
ISSN
2041-1480
Publisher
BioMed Central
Journal / Book Title
Journal of Biomedical Semantics
Volume
7
Copyright Statement
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
License URL
Sponsor
Defence Science and Technology Laboratory (DSTL)
Grant Number
DSTLX-1000085033
Publication Status
Published
Article Number
61