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An Automatic Participant Detection Framework for Event Tracking on Twitter

Faculty of ICT, University of Malta, MSD 2080 Msida, Malta
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Academic Editor: Frank Werner
Algorithms 2021, 14(3), 92; https://doi.org/10.3390/a14030092
Received: 26 February 2021 / Revised: 15 March 2021 / Accepted: 16 March 2021 / Published: 18 March 2021
Topic 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 systems, but also for a future where machines can model and mine events for themselves. View Full-Text
Keywords: information retrieval; automatic participant detection; twitter; event understanding; topic detection and tracking; event modelling information retrieval; automatic participant detection; twitter; event understanding; topic detection and tracking; event modelling
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MDPI and ACS Style

Mamo, N.; Azzopardi, J.; Layfield, C. An Automatic Participant Detection Framework for Event Tracking on Twitter. Algorithms 2021, 14, 92. https://doi.org/10.3390/a14030092

AMA Style

Mamo N, Azzopardi J, Layfield C. An Automatic Participant Detection Framework for Event Tracking on Twitter. Algorithms. 2021; 14(3):92. https://doi.org/10.3390/a14030092

Chicago/Turabian Style

Mamo, Nicholas; Azzopardi, Joel; Layfield, Colin. 2021. "An Automatic Participant Detection Framework for Event Tracking on Twitter" Algorithms 14, no. 3: 92. https://doi.org/10.3390/a14030092

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