Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media

Lixing Zhu, Zheng Fang, Gabriele Pergola, Robert Procter, Yulan He


Abstract
Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering.
Anthology ID:
2022.naacl-main.112
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1566–1580
Language:
URL:
https://aclanthology.org/2022.naacl-main.112
DOI:
10.18653/v1/2022.naacl-main.112
Bibkey:
Cite (ACL):
Lixing Zhu, Zheng Fang, Gabriele Pergola, Robert Procter, and Yulan He. 2022. Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1566–1580, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media (Zhu et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.112.pdf
Code
 somethingx1202/vadet