Identifying schizophrenia stigma on Twitter: a proof of principle model using service user supervised machine learning.

Stigma has negative effects on people with mental health problems by making them less likely to seek help. We develop a proof of principle service user supervised machine learning pipeline to identify stigmatising tweets reliably and understand the prevalence of public schizophrenia stigma on Twitter. A service user group advised on the machine learning model evaluation metric (fewest false negatives) and features for machine learning.

Terms and conditions apply: Critical issues for readability and jargon in mental health depression apps.

Background: Mental health services are turning to technology to ease the resource burden, but privacy policies are hard to understand potentially compromising consent for people with mental health problems. The FDA recommends a reading grade of 8.

Objective: To investigate and improve the accessibility and acceptability of mental health depression app privacy policies.