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dc.contributor.authorMamo, Nicholas-
dc.contributor.authorAzzopardi, Joel-
dc.contributor.authorLayfield, Colin-
dc.identifier.citationMamo, N., Azzopardi, J., & Layfield, C. (2021). An automatic participant detection framework for event tracking on Twitter. Algorithms, 14(3), 92.en_GB
dc.description.abstractTopic Detection and Tracking (TDT) on Twitter emulates human identifying developments in events from a stream of tweets, but while event participants are important for humans to understand what happens during events, machines have no knowledge of them. Our evaluation on football matches and basketball games shows that identifying event participants from tweets is a difficult problem exacerbated by Twitter’s noise and bias. As a result, traditional Named Entity Recognition (NER) approaches struggle to identify participants from the pre-event Twitter stream. To overcome these challenges, we describe Automatic Participant Detection (APD) to detect an event’s participants before the event starts and improve the machine understanding of events. We propose a six-step framework to identify participants and present our implementation, which combines information from Twitter’s pre-event stream and Wikipedia. In spite of the difficulties associated with Twitter and NER in the challenging context of events, our approach manages to restrict noise and consistently detects the majority of the participants. By empowering machines with some of the knowledge that humans have about events, APD lays the foundation not just for improved TDT.en_GB
dc.publisherMDPI AGen_GB
dc.subjectInformation retrievalen_GB
dc.subjectSocial networksen_GB
dc.subjectData setsen_GB
dc.subjectSemantic computingen_GB
dc.subjectComputational intelligenceen_GB
dc.subjectEvent processing (Computer science)en_GB
dc.titleAn automatic participant detection framework for event tracking on twitteren_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
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