Publication
Title
Improving Dutch vaccine hesitancy monitoring via multi-label data augmentation with GPT-3.5
Author
Abstract
In this paper, we leverage the GPT-3.5 language model both using the Chat-GPT API interface and the GPT-3.5 API interface to generate realistic examples of anti-vaccination tweets in Dutch with the aim of augmenting an imbalanced multi-label vaccine hesitancy argumentation classification dataset. In line with previous research, we devise a prompt that, on the one hand, instructs the model to generate realistic examples based on the human dataset (gold standard) and, on the other hand, to assign one or multiple labels to the generated instances. We then augment our gold standard data with the generated examples and evaluate the impact thereof in a cross-validation setting with several state-of-the-art Dutch BERT models. This augmentation technique predominantly shows improvements in F1 for classifying underrepresented classes while increasing the overall recall, paired with a slight decrease in precision for more common classes. Furthermore, we examine how well the synthetic data generalises to human data in the classification task. To our knowledge, we are the first to utilise Chat-GPT and GPT-3.5 for augmenting a Dutch multilabel dataset classification task.
Language
English
Source (book)
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, July 2023; Toronto, Canada
Publication
Toronto : Association for Computational Linguistics , 2023
ISBN
978-1-959429-87-6
DOI
10.18653/V1/2023.WASSA-1.23
Volume/pages
1 (2023) , p. 251-270
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
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Affiliation
Publications with a UAntwerp address
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Creation 20.02.2024
Last edited 23.02.2024
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