AI and NLP for Mental Health Prediction from Social Media: A Decade of Progress, Challenges, and Explainability (2015–2025)

Mental health prediction from social media has gained increasing attention due to the growing availability of user data and advancements in artificial intelligence (AI) and natural language processing (NLP). This review examines research from 2015–2025, highlighting datasets, methodologies, and explainability approaches. Early studies applied traditional machine learning with handcrafted features but faced scalability and language limitations. Deep learning and transformer models such as BERT have since achieved superior performance, though challenges of bias, interpretability, and computational cost persist. Dataset analysis reveals a reliance on Reddit (≈60%), followed by Twitter (≈25%) and smaller contributions from Weibo, Spanish, and Indian corpora, exposing gaps in multilingual coverage. Explainable AI methods (e.g., SHAP, LIME, attention) improve trust and interpretability, yet remain underexplored for non-English contexts. Future work should prioritize inclusive datasets, efficient interpretable models, and multimodal approaches.