Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets
For researchers in mental health NLP, this review identifies limitations and opportunities in non-social media datasets, but is incremental as it only surveys existing work.
This paper presents the first systematic review of non-social media free-text datasets for mental health detection, finding that they are predominantly English and depression-focused, with gaps in diversity and clinical relevance.
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.