A Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental health Depression detection on Social Media Platforms

Abstract

The use of machine learning and deep learning models in detecting depression on social media has become a promising approach to address the challenges in traditional depression diagnosis methods. This systematic literature review examines the recent advancements in the application of these models to analyze social media data for early identification of depressive symptoms. The review highlights the advantages of leveraging user-generated content on social media platforms, as well as the potential of large language models and neural networks in achieving high accuracy in depression detection. However, the review also discusses the need to address challenges related to the interpretability and transparency of these models, and the importance of integrating them with clinical data and comprehensive mental health monitoring frameworks. The findings of this review provide valuable insights for researchers and healthcare professionals interested in utilizing innovative technologies to enhance the early detection and management of depression. Social media platforms have emerged as a valuable source of data for analyzing various mental health conditions, including depression. Researchers have explored the potential of leveraging textual content from social media posts to detect and monitor depressive disorders, as individuals often express their mental health struggles and experiences on these platforms. This paper presents a comprehensive literature review on the current state of research in machine learning models for mental health analysis on social media.