Prediction of Suicidal Tendencies using Big Data Analytics on Social Media

Abstract

The rapid growth of social media platforms has created unprecedented opportunities for large-scale behavioral and linguistic analysis of mental health signals. Advances in big-data infrastructure, natural language processing (NLP), and machine learning (ML) have enabled the development of systems capable of detecting suicidal ideation and estimating short-term risk trajectories from online activity. Empirical studies demonstrate promising predictive performance across platforms such as Twitter, Facebook, and Reddit; however, these capabilities raise significant methodological, ethical, and governance challenges that necessitate cautious deployment and continuous human oversight [1], [12], [16].