  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.
