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.

翻译：心理健康障碍对全球人群产生显著影响，无论其背景、教育程度或社会经济地位如何。然而，获得充分护理仍是一项挑战，尤其在资源有限的欠发达社区。文本挖掘工具通过辅助专业人员诊断和治疗患者，为支持心理健康护理提供了巨大潜力。本研究旨在解决阿拉伯语心理健康资源稀缺的问题，以开发此类工具。我们提出MentalQA——一个新颖的阿拉伯语数据集，包含对话式问答（QA）交互。为确保数据质量，我们采用明确定义的标注模式并实施质量控制措施，进行了严格的标注流程。数据收集自一个问答型医疗平台。心理健康问题及其对应答案的标注模式借鉴了现有分类框架并进行了适当调整。问题类型涵盖六种不同类别：诊断、治疗、解剖与生理学、流行病学、健康生活方式及服务提供者选择。回答策略包括信息提供、直接指导与情感支持。三名经验丰富的标注员协作完成数据标注以确保一致性。结果显示标注员间一致性较高：问题类型的Fleiss' Kappa值为0.61，回答策略的Fleiss' Kappa值为0.98。深入分析揭示了具有洞察力的模式，包括不同年龄段人群的问题偏好差异，以及问题类型与回答策略之间的强相关性。MentalQA为开发能够支持心理健康专业人员及信息寻求者的阿拉伯语文本挖掘工具奠定了宝贵基础。