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

TL;DR

MentalQA is a newly created Arabic dataset of mental health questions and answers, annotated with detailed schema, aiming to support the development of Arabic text mining tools for mental healthcare.

Contribution

The paper introduces MentalQA, a novel annotated Arabic mental health QA dataset with a rigorous annotation process and high inter-annotator agreement, filling a critical resource gap.

Findings

High inter-annotator agreement (Fleiss' Kappa 0.61 for question types, 0.98 for answer strategies)

Identified patterns in question preferences across age groups

Strong correlation between question types and answer strategies

Abstract

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.