On Informative Tweet Identification for Tracking Mass Events

Renato João

Abstract

Twitter has been heavily used as an important channel for communicating and discussing about events in real-time. In such major events, many uninformative tweets are also published rapidly by many users, making it hard to follow the events. In this paper, we address this problem by investigating machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We examine both traditional approaches with a rich set of handcrafted features and state of the art approaches with automatically learned features. We further propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former and our proposed model performs the best, suggesting highly effective mechanisms for tracking mass events.

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Paper Citation


in Harvard Style

João R. (2021). On Informative Tweet Identification for Tracking Mass Events.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 1266-1273. DOI: 10.5220/0010392712661273


in Bibtex Style

@conference{icaart21,
author={Renato João},
title={On Informative Tweet Identification for Tracking Mass Events},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={1266-1273},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010392712661273},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - On Informative Tweet Identification for Tracking Mass Events
SN - 978-989-758-484-8
AU - João R.
PY - 2021
SP - 1266
EP - 1273
DO - 10.5220/0010392712661273