Mining Reasons For And Against Vaccination From Unstructured Data Using Nichesourcing and AI Data Augmentation
This work addresses the challenge of extracting subjective vaccination-related reasons from non-structured data, which is incremental as it builds on existing AI and data augmentation techniques for a specific domain.
The researchers tackled the problem of predicting reasons for and against vaccination from unstructured text by creating the RFAV dataset, annotated through nichesourcing and augmented with GPT-4 and GPT-3.5-Turbo, and explored the impact of this artificially augmented data using in-context learning.
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.