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

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1566 - 1580 July 10-15, 2022 ©2022 Association for Computational Linguistics Disentangled Learning of Stance and Aspect Topics for Vaccine Attitude Detection in Social Media Lixing Zhu1, Zheng Fang1, Gabriele Pergola1, Rob Procter1,2, Yulan He1,2 1Department of Computer Science, University of Warwick, UK 2The Alan Turing Institute, UK {lixing.zhu,z.fang.4,gabriele.pergola.1 rob.procter,yulan.he}@warwick.ac.uk Abstract Building models to detect vaccine attitudes on social media is challenging because of the com- posite, 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 unan- notated 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 atti- tudes. 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 de- tection and tweet clustering. Our source code and dataset are available at http://github. com/somethingx1202/VADet. 1 Introduction The aim of vaccine attitude detection in social me- dia is to extract people’s opinions towards vaccines by analysing their online posts. This is closely re- lated to aspect-based sentiment analysis in which both aspects and related sentiments need to be identified. Previous research has been largely fo- cused on product reviews and relied on aspect- level sentiment annotations to train models (Barnes et al., 2021), where aspect-opinions are extracted as triples (Peng et al., 2020), polarized targets (Ma et al., 2018) or sentiment spans (He et al., 2019). However, for the task of vaccine attitude detec- tion on Twitter, such a volume of annotated data is barely available (Kunneman et al., 2020; Paul et al., The AstraZeneca one is rough for up to 48 hours; after that you may still be a bit swollen but you'll basically feel fine. I've had that and the virus, and the vaccine is far less unpleasant. Have felt for the past 24 hours that I’ve been run over by three double decker buses after the AstraZeneca vaccine yesterday morning. Starting to feel a little normal now but it’s not been nice! This is quite baffling. I got my second Pfizer vaccine last week and I have gone totally off chocolate! As side effects go, it’s not so bad. There are some very interesting ties between this vaccines creators and the eugenics movement which is concerning considering it’s mainly been promoted as a vaccine for poor folks in the third world. Figure 1: Top: Expressions of aspects entangled with expressions of opinions. …

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