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.

翻译：我们提出了支持与反对疫苗接种理由数据集，这是一个用于预测支持与反对疫苗接种的理由及其所引用的科学权威的数据集，该数据集通过利基众包进行标注，并利用GPT4与GPT3.5-Turbo进行了数据增强。我们展示了如何在非结构化文本中，依据不同的任务定义，挖掘这些理由——尽管涉及高度的主观性——并探究了使用GPT4与GPT3.5-Turbo进行上下文学习所生成的人工增强数据的影响。我们发布了该数据集、训练好的模型，以及用于训练标注者和定义任务的标注手册。