# Mental Health Disorder Detection Beyond Social Media: A Systematic Review of Available Datasets

arXiv: https://arxiv.org/abs/2607.03540 · HTML: https://mlsanity.com/paper/2607.03540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 115 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 11 of 11)

- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based Model](https://mlsanity.com/paper/2202.08210/) (2022)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Detecting Cross-Geographic Biases in Toxicity Modeling on Social Media](https://mlsanity.com/paper/2104.06999/) (2021)
- [Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models](https://mlsanity.com/paper/2311.04915/) (2023)
- [DEPAC: a Corpus for Depression and Anxiety Detection from Speech](https://mlsanity.com/paper/2306.12443/) (2023)
- [Detecting Depression in Thai Blog Posts: a Dataset and a Baseline](https://mlsanity.com/paper/2111.04574/) (2021)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [Enhancing Depression Diagnosis with Chain-of-Thought Prompting](https://mlsanity.com/paper/2408.14053/) (2024)
- [A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection](https://mlsanity.com/paper/2501.00129/) (2024)

## Abstract (arXiv)

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.