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Title: ELD : event timeline detection - a participant-based approach to tracking events
Authors: Mamo, Nicholas
Azzopardi, Joel
Layfield, Colin
Keywords: Social networks
Data sets
Information retrieval
Semantic computing
Computational intelligence
Issue Date: 2019
Publisher: Association for Computing Machinery
Citation: Mamo, N., Azzopardi, J., & Layfield, C. (2019, September). ELD: Event TimeLine Detection--A Participant-Based Approach to Tracking Events. Proceedings of the 30th ACM Conference on Hypertext and Social Media, Germany. 267-268.
Abstract: People all over the world talk as events unfold, but what does it take for a machine to truly track an event? Event TimeLine Detection (ELD) is a real-time Topic Detection and Tracking (TDT) solution to track events using Twitter with the hypothesis that it takes a deeper understanding of the event’s domain for a machine to describe its evolution. In ELD, understanding takes the form of identifying the participants that would eventually drive the event’s evolution. We propose Automatic Participant Detection (APD) as a way of identifying event participants, which ELD then tracks during the proceedings. TDT then mines the resulting Twitter stream, extracting developments and describing them as a timeline using a summarization algorithm.
ISBN: 97814503688581909.
Appears in Collections:Scholarly Works - FacICTAI

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