Fine-grained Topic Detection and Tracking on Twitter

Nicholas Mamo, Joel Azzopardi, Colin Layfield

2021

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 overcome 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.

Paper Citation

in Harvard Style

Mamo N., Azzopardi J. and 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) - Volume 1: KDIR; ISBN 978-989-758-533-3, SciTePress, pages 79-86. DOI: 10.5220/0010639600003064

in Bibtex Style

@conference{kdir21,
author={Nicholas Mamo and Joel Azzopardi and Colin Layfield},
title={Fine-grained Topic Detection and Tracking on Twitter},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR},
year={2021},
pages={79-86},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010639600003064},
isbn={978-989-758-533-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - Volume 1: KDIR
TI - Fine-grained Topic Detection and Tracking on Twitter
SN - 978-989-758-533-3
AU - Mamo N.
AU - Azzopardi J.
AU - Layfield C.
PY - 2021
SP - 79
EP - 86
DO - 10.5220/0010639600003064
PB - SciTePress