Analysing Mental Health Through Social Media and Computational Linguistics

Mental health disorders are a growing concern in today’s digital age, with social media platforms serving as a reflection of user’s mental health states. It is crucial to explore the underlying causes that drive individuals of all ages toward depression and identify effective ways to encourage them to choose life. In today's digital era, social media serves as a significant platform where people express their emotions, daily activities, and thoughts. This has led to the question of whether analyzing social media content can help determine an individual's emotional state, particularly identifying distress levels that may indicate suicidal tendencies. This research explores the application of computational linguistics and machine learning techniques to predict mental health conditions based on social media text input. The system processes user-generated content and timestamps to classify mental health states using various classifiers, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Logistic Regression, and others. Our approach leverages natural language processing (NLP) and deep learning models to analyze linguistic patterns associated with mental health indicators such as depression, anxiety, and stress. The proposed framework offers a novel, automated method for early mental health assessment, contributing to digital mental health monitoring and intervention strategies.