# Towards Detecting Cascades of Biased Medical Claims on Twitter

arXiv: https://arxiv.org/abs/2312.15040 · HTML: https://mlsanity.com/paper/2312.15040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model](https://mlsanity.com/paper/2008.06460/) (2020)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022)
- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [Fairness via AI: Bias Reduction in Medical Information](https://mlsanity.com/paper/2109.02202/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation](https://mlsanity.com/paper/2409.07424/) (2024)

## Abstract (arXiv)

Social media may disseminate medical claims that highlight misleading correlations between social identifiers and diseases due to not accounting for structural determinants of health. Our research aims to identify biased medical claims on Twitter and measure their spread. We propose a machine learning framework that uses two models in tandem: RoBERTa to detect medical claims and DistilBERT to classify bias. After identifying original biased medical claims, we conducted a retweet cascade analysis, computing their individual reach and rate of spread. Tweets containing biased claims were found to circulate faster and further than unbiased claims.