We present Reasons For and Against Vaccination (RFAV), a dataset for predicting reasons for and against vaccination, and scientific authorities used to justify them, annotated through nichesourcing and augmented using GPT4 and GPT3.5-Turbo. We show how it is possible to mine these reasons in non-structured text, under different task definitions, despite the high level of subjectivity involved and explore the impact of artificially augmented data using in-context learning with GPT4 and GPT3.5-Turbo. We publish the dataset and the trained models along with the annotation manual used to train annotators and define the task.
Cite
@article{arxiv.2406.19951,
title = {Mining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentation},
author = {Damián Ariel Furman and Juan Junqueras and Z. Burçe Gümüslü and Edgar Altszyler and Joaquin Navajas and Ophelia Deroy and Justin Sulik},
journal= {arXiv preprint arXiv:2406.19951},
year = {2024}
}