  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.
