Towards Detecting Cascades of Biased Medical Claims on Twitter
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 18 references that are in the map. First posted on arXiv 22 Dec 2023.
Builds on
7 of 18 references.
References · 7
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2016Parsing Argumentation Structures in Persuasive Essayscited by 99
- 2020Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT modelcited by 87
- 2021LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Contentcited by 22
- 2022Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twittercited by 19
- 2021Claim Detection in Biomedical Twitter Postscited by 18
- 2021Fairness via AI: Bias Reduction in Medical Informationcited by 4
Built on by
Citing papers per year: 2024: 1.
1 paper.