Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence

TL;DR

This paper presents a system for aligning social media medical claims with evidence from medical literature, introducing a synthetic dataset to improve claim identification, extraction, and evidence retrieval.

Contribution

It introduces a novel system and a synthetic dataset, EMCC, to enhance the process of verifying medical claims on social media against scientific evidence.

Findings

Improved accuracy in claim identification and evidence retrieval.

Synthetic dataset enhances model performance across tasks.

System aids non-experts in assessing medical claim validity.

Abstract

Evidence-based medicine is the practice of making medical decisions that adhere to the latest, and best known evidence at that time. Currently, the best evidence is often found in the form of documents, such as randomized control trials, meta-analyses and systematic reviews. This research focuses on aligning medical claims made on social media platforms with this medical evidence. By doing so, individuals without medical expertise can more effectively assess the veracity of such medical claims. We study three core tasks: identifying medical claims, extracting medical vocabulary from these claims, and retrieving evidence relevant to those identified medical claims. We propose a novel system that can generate synthetic medical claims to aid each of these core tasks. We additionally introduce a novel dataset produced by our synthetic generator that, when applied to these tasks, demonstrates not only a more flexible and holistic approach, but also an improvement in all comparable metrics. We make our dataset, the Expansive Medical Claim Corpus (EMCC), available at https://zenodo.org/records/8321460