Mining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentation
<|reference_start|>Mining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentation: 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.<|reference_end|>
@article{furman2024mining,
    title={Mining Reasons For And Against Vaccination From Unstructured Data Using
  Nichesourcing and AI Data Augmentation},
    author={Dami'an Ariel Furman, Juan Junqueras, Z. Burc{c}e G"um"usl"u,
  Edgar Altszyler, Joaquin Navajas, Ophelia Deroy, Justin Sulik},
    journal={arXiv preprint arXiv:2406.19951},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19951},
    primaryClass={cs.CL}
}