A Computational Model for Depression Analysis in Social Media Data

• Alexandre Augusto Foppa Unisinos
• Jorge Luis Vitória Barbosa Unisinos

DOI: https://doi.org/10.5753/sbcas.2025.6939

Abstract

Mental health disorders affect millions, compromise quality of life, and reduce productivity. Depression impacts around 5% of adults worldwide and costs nearly US$1 trillion annually. We propose Serapis, a computational model that organizes data from social networks, questionnaires, and the PHQ-9 to produce actionable intelligence for depression diagnosis. Our model integrates LLMs with an ontology built on best practices in forensic computing and OSINT. We developed an MVP prototype that builds a timeline of posts and highlights entries relevant to psychologists. A focus group with professionals validated the system’s usability, reporting high ease of use and perceived utility, and recommended improvements in contextual accuracy and collaboration features.

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