Detecting Depression on Social Media : A Comprehensive Review of Data Analysis, Deep Learning, NLP, and Machine Learning Approaches

Abstract

Social media platforms are vast reservoirs of human sentiment and behavior, making them ripe for depression detection. This literature review delves into approaches for this detection using data analysis, deep learning, natural language processing (NLP), and machine learning (ML). We discuss data types used and explore deep learning techniques like CNN, RNN, and DNN, applied across platforms such as Facebook, Twitter, and Reddit. The review also highlights NLP's role and ML algorithms, notably SVM, Naive Bayes, K-Nearest Neighbour, Random Forest, and Decision Trees. We analyze depression causes, its link with social media, and variations across age and gender. This comprehensive study guides researchers and practitioners in technology-driven mental health solutions.