Analyzing social media using AI identify signs of depression in white Americans

 Analyzing social media using AI identify signs of depression in white Americans

A recent study suggests that analyzing social media using artificial intelligence (AI) may identify signs of depression in white Americans but not in their Black counterparts. This finding underscores the importance of considering diverse racial and ethnic groups when training AI models for healthcare tasks.

The study, published in PNAS, found that an AI model used to analyze language in social media posts was significantly less predictive for depression in Black individuals compared to white individuals on Meta Platforms’ Facebook.

While previous research indicated that certain language patterns, such as frequent use of first-person pronouns and self-deprecating terms, are linked to depression, these associations were found to apply mainly to white individuals in the new study.

Researchers analyzed posts from 868 volunteers, including equal numbers of Black and white adults with similar demographics. Participants also completed a depression screening questionnaire commonly used by healthcare providers.

The study’s co-author, Sharath Chandra Guntuku, noted that language patterns related to depression differed between white and Black individuals, with self-focused attention and self-deprecation being more relevant for white individuals.

While social media data cannot diagnose depression, it can aid in risk assessment for individuals or groups. Previous research by the same team used social media language to assess community mental health during the Covid-19 pandemic.

Brenda Curtis, from the US National Institute on Drug Abuse, highlighted the potential of social media language to predict treatment outcomes in patients with substance abuse disorders.

Overall, the study emphasizes the need for AI models in healthcare to consider diverse populations to ensure equitable and effective performance across different demographic groups.

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