Please use this identifier to cite or link to this item:
Title: Fine-grained topic detection and tracking on Twitter
Authors: Mamo, Nicholas
Azzopardi, Joel
Layfield, Colin
Keywords: Twitter
Topic distillation (Internet searching)
Information retrieval
Keyword searching
Electronic information resource searching
Issue Date: 2021
Publisher: SCITEPRESS – Science and Technology Publications, Lda
Citation: Mamo, N., Azzopardi, J., & Layfield, C. (2021). Fine-grained Topic Detection and Tracking on Twitter. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR. 79-86.
Abstract: With its large volume of data and free access to information, Twitter revolutionised Topic Detection and Tracking (TDT). Thanks to Twitter, TDT could build timelines of real-world events in real-time. However, over the years TDT struggled to adapt to Twitter’s noise. While TDT’s solutions stifled noise, they also kept the area from building granular timelines of events, and today, TDT still relies on large datasets from popular events. In this paper, we detail Event TimeLine Detection (ELD) as a solution: a real-time system that combines TDT’s two broad approaches, document-pivot and feature-pivot methods. In ELD, an on-line document-pivot technique clusters a stream of tweets, and a novel feature-pivot algorithm filters clusters and identifies topical keywords. This mixture allows ELD to over come the technical limitations of traditional TDT algorithms to build fine-grained timelines of both popular and unpopular events. Nevertheless, our results emphasize the importance of robust topic tracking and the ability to filter subjective content.
Appears in Collections:Scholarly Works - FacICTAI

Files in This Item:
File Description SizeFormat 
  Restricted Access
469.13 kBAdobe PDFView/Open Request a copy

Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.