Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets
2026-07-03 • Computation and Language
Computation and Language
AI summaryⓘ
The authors looked at studies that use written text not from social media to help find mental health problems like depression. They found most of these texts are in English and mostly focus on depression. The texts come from different places and are labeled in various ways. The authors point out there are still many gaps and chances to make better and more useful datasets for mental health research.
mental health detectionnatural language processingmachine learningfree-text datasetsdepressionPRISMA methodologysampling biasannotation techniquesnon-social media data
Authors
Sadiya Sayara Chowdhury Puspo, Ana-Maria Bucur, Stevie Chancellor, Özlem Uzuner, Marcos Zampieri
Abstract
Detecting mental health disorders in a timely manner is an important societal challenge. NLP and machine learning (ML) methods used to assist with detection rely on data collected primarily from social media. However, such datasets often have sampling biases and inherent ethical and privacy issues. One avenue to overcome these limitations is non-social media data. We present the first comprehensive review of non-social media, free-text datasets for mental health research. We use the PRISMA methodology to conduct our survey and we review datasets available in multiple languages. We find that non-social media free-text based datasets are predominantly focused on English and on detecting depression. These datasets also vary in demographics, platforms, data types, annotation techniques, and methodologies. This systematic review also reveals key gaps and highlights opportunities to develop more diverse, reliable and clinically-relevant resources.