# MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare

arXiv: https://arxiv.org/abs/2405.12619 · HTML: https://mlsanity.com/paper/2405.12619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 7 of 7)

- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [EmoMent: An Emotion Annotated Mental Health Corpus from two South Asian Countries](https://mlsanity.com/paper/2208.08486/) (2022)
- [LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts](https://mlsanity.com/paper/2306.05596/) (2023)
- [Identifying Mentions of Pain in Mental Health Records Text: A Natural Language Processing Approach](https://mlsanity.com/paper/2304.01240/) (2023)

## Built on by (most cited first, 4 of 4)

- [MHQA: A Diverse, Knowledge Intensive Mental Health Question Answering Challenge for Language Models](https://mlsanity.com/paper/2502.15418/) (2025)
- [CARMA: Comprehensive Automatically-annotated Reddit Mental Health Dataset for Arabic](https://mlsanity.com/paper/2511.03102/) (2025)
- [AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering](https://mlsanity.com/paper/2508.20047/) (2025)
- [Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care](https://mlsanity.com/paper/2406.15966/) (2024)

## Abstract (arXiv)

Mental health disorders significantly impact people globally, regardless of background, education, or socioeconomic status. However, access to adequate care remains a challenge, particularly for underserved communities with limited resources. Text mining tools offer immense potential to support mental healthcare by assisting professionals in diagnosing and treating patients. This study addresses the scarcity of Arabic mental health resources for developing such tools. We introduce MentalQA, a novel Arabic dataset featuring conversational-style question-and-answer (QA) interactions. To ensure data quality, we conducted a rigorous annotation process using a well-defined schema with quality control measures. Data was collected from a question-answering medical platform. The annotation schema for mental health questions and corresponding answers draws upon existing classification schemes with some modifications. Question types encompass six distinct categories: diagnosis, treatment, anatomy & physiology, epidemiology, healthy lifestyle, and provider choice. Answer strategies include information provision, direct guidance, and emotional support. Three experienced annotators collaboratively annotated the data to ensure consistency. Our findings demonstrate high inter-annotator agreement, with Fleiss' Kappa of 0.61 for question types and 0.98 for answer strategies. In-depth analysis revealed insightful patterns, including variations in question preferences across age groups and a strong correlation between question types and answer strategies. MentalQA offers a valuable foundation for developing Arabic text mining tools capable of supporting mental health professionals and individuals seeking information.