Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/103143
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. |
URI: | https://www.um.edu.mt/library/oar/handle/123456789/103143 |
ISBN: | 97814503688581909. |
Appears in Collections: | Scholarly Works - FacICTAI |
Files in This Item:
File | Description | Size | Format | |
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ELD__event_timeline_detection_-_a_participant-based_approach_to_tracking_events(2019).pdf Restricted Access | 738.74 kB | Adobe PDF | View/Open Request a copy |
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