Analysis of social media language using AI models predicts depression severity for white Americans, but not Black Americans

NIH-supported study also found Black people with depression used different language compared to white people to express their thoughts on Facebook

Researchers were able to predict depression severity for white people, but not for Black people using standard language-based computer models to analyze Facebook posts. Words and phrases associated with depression, such as first-person pronouns and negative emotion words, were around three times more predictive of depression severity for white people than for Black people. The study, published today in the Proceedings of the National Academy of Sciences, is co-authored by researchers at the University of Pennsylvania, Philadelphia, and the National Institute on Drug Abuse (NIDA), part of the National Institutes of Health (NIH), which also funded the study.

While previous research has indicated that social media language could provide useful information as part of mental health assessments, the findings from this study point to potential limitations in generalizing this practice by highlighting key demographic differences in language used by people with depression. The results also highlight the importance of including diverse pools of data to ensure accuracy as machine learning models, an application of artificial intelligence (AI) language models, are developed.

“As society explores the use of AI and other technologies to help deliver much-needed mental health care, we must ensure no one is left behind or misrepresented,” said Nora Volkow, M.D., NIDA director. “More diverse datasets are essential to ensure that healthcare disparities are not perpetuated by AI and that these new technologies can help tailor more effective health care interventions.”

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This page was last updated on Tuesday, March 26, 2024