Human-Centered Data Analytics for Identifying Mental Health Risks in Digital Communities

We introduce Human-Centered Data Analytics (HCDA), a method for identifying online mental health issues that blends computational techniques with user-centered design principles to produce insights that are practical, interpretable, and morally acceptable. The method entails extracting anonymized text and interactional data from online public spaces, finding characteristics linked to stress, anxiety, and depression, and then using machine learning (ML) in conjunction with user-centered validation techniques to extract first recognition patterns. According to the study's findings, HCDA is more accurate and interpretable than conventional data-driven models at detecting early indicators of mental health disorders. Interpreting risk variables contextually is also informed by user interaction observations. In order to provide proactive, ethical solutions for members of the digital community, the work highlights the value of combining quantitative analysis with human-centered viewpoints. It also has practical implications for mental health experts, platform developers, and policy makers.

Human-Centered Data Analytics, Mental Health

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