Scoles and Nicodemo used computer analysis (natural language processing) to analyze the subreddit r/Medicine. They focused on a set of chronic conditions that are stigmatized and compared them to conventionally accepted conditions. ME/CFS, depression, and Lyme were the ‘winners’ in terms of being the most stigmatized.
Isn’t that wonderful?
The paper
https://www.sciencedirect.com/science/article/pii/S016726812200347X
Abstract
This study uses machine learning and natural language processing tools to examine the language used by healthcare professionals on a global online forum. It contributes to an underdeveloped area of knowledge, that of physician attitudes toward their patients. Using comments left by physicians on Reddit’s “Medicine” subreddit (r/medicine), we test if the language from online discussions can reveal doctors’ attitudes toward specific medical conditions. We focus on a set of chronic conditions that usually are more stigmatized and compare them to ones well accepted by the medical community. We discovered that when comparing diseases with similar traits, doctors discussed some conditions with more negative attitudes. These results show bias does not occur only along the dimensions traditionally analyzed in the economics literature of gender and race, but also along the dimension of disease type. This is meaningful because the emotions associated with beliefs impact physicians’ decision making, prescribing behavior, and quality of care. First, we run a binomial LASSO-logistic regression to compare a range of 21 diseases against myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), depression, and the autoimmune diseases multiple sclerosis and rheumatoid arthritis. Next, we use dictionary methods to compare five more chronic diseases: Lyme disease, Ehlers-Danlos syndrome (EDS), Alzheimer’s disease, osteoporosis, and lupus. The results show physicians discuss ME/CFS, depression, and Lyme disease with more negative language than the other diseases in the set. The results for ME/CFS included over four times more negative words than the results for depression.