Abstract
BackgroundTwitter 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.ResultsIn 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.ConclusionsThis 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.
Highlights
Updates posted on social media platforms such as Twitter contain a great deal of information about events in the physical world, with the majority of topics discussed on Twitter being news related [1]
Applications of event detection and summarisation on Twitter have included the detection of disease outbreaks [2], natural disasters such as earthquakes [3] and reaction to sporting events [4]
Existing disease outbreak detection algorithms have been applied to Twitter data, for example in a case study [15] of a non-seasonal disease outbreak of Enterohemorrhagic Escherichia coli (EHEC) in Germany
Summary
Updates posted on social media platforms such as Twitter contain a great deal of information about events in the physical world, with the majority of topics discussed on Twitter being news related [1]. Twitter updates 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 be highly useful for event detection and situational awareness applications. Other approaches have aimed to pick up more localised events These have included searching for spatial clusters in tweets [9], leveraging the social network structure [10], analysing the patterns of communication activity [11] and identifying significant keywords by their spatial signature [12]. Existing disease outbreak detection algorithms have been applied to Twitter data, for example in a case study [15] of a non-seasonal disease outbreak of Enterohemorrhagic Escherichia coli (EHEC) in Germany They searched for tweets from Germany matching the keyword “EHEC”, and used the daily tweet counts as input to their epidemic detection algorithms. Our study uses a modified and generalised version of this event detection approach
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