Bias-Aware Systematic Review of AI-Based Mental Health Detection Using Social Media: A PRISMA and PROBAST(+AI) Analysis

Abstract

This study presents a bias-aware systematic review of artificial intelligence (AI)-based mental health detection using social media data from 2015 to 2025. Guided by PRISMA 2020 and PROBAST(+AI), records from IEEE Xplore, Scopus, and Web of Science were screened from 3,861 initial records to 308 included studies. Results show a strong shift toward transformer and hybrid models, depression-focused tasks, and Twitter/X and Reddit datasets. However, the corpus also shows major methodological gaps, including absent external validation, limited reporting of class imbalance handling, low explainable AI adoption, and underreported platform sources. The review argues that future systems require transparent data provenance, bias-aware validation, explainable decision support, and privacy-preserving crossplatform evaluation before clinical or public-health deployment.