Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 13th International Conference on Agents and Artificial Intelligence |
| Subtitle of host publication | ICAART 2021 |
| Editors | Ana Paula Rocha, Luc Steels, Jaap van den Herik |
| Pages | 1266-1273 |
| Number of pages | 8 |
| ISBN (electronic) | 9789897584848 |
| Publication status | Published - 2021 |
| Event | 13th International Conference on Agents and Artificial Intelligence, ICAART 2021 - Virtual, Online, Austria Duration: 4 Feb 2021 → 6 Feb 2021 |
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.
Keywords
- Classification, Deep learning, Machine learning
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Software
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Proceedings of the 13th International Conference on Agents and Artificial Intelligence: ICAART 2021. ed. / Ana Paula Rocha; Luc Steels; Jaap van den Herik. 2021. p. 1266-1273.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - On Informative Tweet Identification for Tracking Mass Events
AU - João, Renato Stoffalette
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Classification
KW - Deep learning
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85103848643&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2101.05656
DO - 10.48550/arXiv.2101.05656
M3 - Conference contribution
AN - SCOPUS:85103848643
SP - 1266
EP - 1273
BT - Proceedings of the 13th International Conference on Agents and Artificial Intelligence
A2 - Rocha, Ana Paula
A2 - Steels, Luc
A2 - van den Herik, Jaap
T2 - 13th International Conference on Agents and Artificial Intelligence, ICAART 2021
Y2 - 4 February 2021 through 6 February 2021
ER -