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

arXiv: https://arxiv.org/abs/2405.11219 · HTML: https://mlsanity.com/paper/2405.11219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 18 May 2024.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only](https://mlsanity.com/paper/2306.01116/) (2023)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://mlsanity.com/paper/2105.13626/) (2021)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Learning Dense Representations for Entity Retrieval](https://mlsanity.com/paper/1909.10506/) (2019)
- [COMETA: A Corpus for Medical Entity Linking in the Social Media](https://mlsanity.com/paper/2010.03295/) (2020)
- [Scientific Claim Verification with VERT5ERINI](https://mlsanity.com/paper/2010.11930/) (2020)
- [Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training](https://mlsanity.com/paper/2010.12688/) (2020)
- [RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media](https://mlsanity.com/paper/2210.06331/) (2022)
- [Mining social media data for biomedical signals and health-related behavior](https://mlsanity.com/paper/2001.10285/) (2020)
- ['UFAL at MultiLexNorm 2021: Improving Multilingual Lexical Normalization by Fine-tuning ByT5](https://mlsanity.com/paper/2110.15248/) (2021)
- [Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets](https://mlsanity.com/paper/1904.05308/) (2019)
- [AILAB-Udine@SMM4H 22: Limits of Transformers and BERT Ensembles](https://mlsanity.com/paper/2209.03452/) (2022)

## Abstract (arXiv)

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