Mental Wellbeing Assessment Through Social Media and Machine Learning – A Web-Based Tool

Mental health disorders such as depression and suicidal ideation affect hundreds of millions globally, posing serious public health challenges. With the rise of social media platforms, user-generated content has become a valuable source for detecting psychological distress through computational methods.This study presents a comprehensive roadmap for predicting mental health conditions using social media data, focusing on machine learning and deep learning approaches. We examine key components of the predictive pipeline, including data collection, feature extraction, and classification algorithms. Recent research efforts are reviewed to highlight effective techniques for identifying at-risk individuals based on linguistic, behavioral, and interactional patterns online. The paper also discusses the development of automated detection systems, ethical considerations, and future research directions. These advances support scalable, real-time mental health monitoring and offer potential to augment traditional screening and intervention strategies.