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Title: An automatic participant detection framework for event tracking on twitter
Authors: Mamo, Nicholas
Azzopardi, Joel
Layfield, Colin
Keywords: Information retrieval
Social networks
Data sets
Semantic computing
Computational intelligence
Event processing (Computer science)
Issue Date: 2021
Publisher: MDPI AG
Citation: Mamo, N., Azzopardi, J., & Layfield, C. (2021). An automatic participant detection framework for event tracking on Twitter. Algorithms, 14(3), 92.
Abstract: 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.
ISSN: 1999-4893
Appears in Collections:Scholarly Works - FacICTAI

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