Depression Detection In Social Media User Using Deep Learning

Abstract

Depression is a very severe and grave mental disorder, which is affecting most of the population nowadays because of various reasons like stress at work, school, college, personal life, other diseases, etc. It is also called as Major depressive disorder. Depression is a significant mental health concern that affects a vast number of individuals worldwide. With the rise of social media, people often express their emotions and struggles through online platforms, providing an opportunity for early depression detection using advanced computational techniques.

This project proposes a deep learning-based approach using Long Short-Term Memory (LSTM) networks to analyze user-generated content and identify depressive tendencies. The system is designed to classify words related to depression in social media posts and provide automated motivational responses to users. The proposed system enhances mental health support by generating link-based alerts for the user's most interacted social media friends when severe depressive content is detected. This feature ensures timely awareness and potential intervention from close contacts, offering a supportive network for individuals at risk. By integrating LSTM-based classification with real-time monitoring and social connectivity, this project aims to provide a scalable and non-intrusive method for depression detection and mental health awareness in the digital age.