Depression in Black People Goes Unnoticed by AI Models Analyzing Language in Social Media Posts

Image of an African-American hand holding a cell phone

Methods researchers developed to detect possible depression through language in social media posts don’t appear to work when applied to posts by Black people on social media, according to a new analysis by researchers from Penn’s Perelman School of Medicine and its School of Engineering and Applied Science. The research, published in PNAS, points to an area to focus on for significant improvement and amplifies the importance of considering the intersection of race, health risks, and social media.

Work in the past uncovered that using first-person pronouns in posts (“I”) and certain categories of words (self-deprecating terms and expressing outsider feelings) in social media posts was predictive of depression among people who use social media. However, in analyzing Facebook posts from more than 800 people—a sample that included equal numbers of Black and white individuals, some who reported having depression and some who did not—the researchers found that the predictive qualities of the “predictive” words applied mainly to white people on social media.

“We were surprised that these language associations found in numerous prior studies didn’t apply across the board,” said one of the study’s senior authors, Sharath Chandra Guntuku, PhD, a researcher in the Center for Insights to Outcomes at Penn Medicine and an assistant professor (research) of Computer and Information Science in Penn Engineering. “We need to have the understanding that, when thinking about mental health and devising interventions for treatment, we should account for the differences among racial groups and how they may talk about depression. We cannot put everyone in the same bucket.”

This story was written by Frank Otto. To read the full article, please visit Penn Medicine.