Ethically Driven Synthetic Data for Suicidal Ideation Detection sing NLP

Detecting suicidal ideation through social media content is a critical initiative to support mental health intervention strategies. This study presents an explainable framework that leverages advanced natural language processing (NLP) techniques to address the challenges of identifying suicidal intent in user- generated content. A significant innovation in this work is the creation of synthetic datasets informed by psychological and social factors associated with suicidal ideation, designed to supplement limited real-world data while maintaining ethical considerations. The proposed system classifies social media content into two categories: non-suicidal or suicidal. The hybrid approach of combining synthetic and real-world data enhances model performance, achieving superior accuracy and robustness compared to traditional methods. The framework emphasizes explainability by incorporating techniques that identify key linguistic and contextual features driving model predictions, ensuring interpretability for mental health professionals and researchers. This approach underscores the potential of integrating synthetic data and NLP in addressing real-world challenges such as data scarcity, diversity, and ethical concerns. By providing actionable insights and ensuring transparency, the proposed framework contributes to building reliable and scalable solutions for suicide prevention in digital environments.