ContextMental: A Sociocultural Benchmark for Arabic Mental Health Understanding

Mental health discourse may reflect social relationships, cultural norms, and religious factors that shape how individuals express and interpret distress. Existing NLP research on mental health has advanced the detection of depression, anxiety, suicide risk, and related clinical signals using text mining, neural classification, transformer-based models, and, more recently, large language models. However, most systems treat text primarily as a clinical signal rather than examining the social and cultural contexts in which distress is expressed. Arabic NLP research remains even more limited, largely focusing on detecting clinical conditions while overlooking contextual factors that shape mental health questions. This work introduces ContextMental, a multi-label annotation schema and benchmark dataset for modeling sociocultural context in Arabic mental health questions. The dataset contains 2677 questions, including 552 instances with contextual labels, enabling fine-grained analysis of social, cultural, and religious dimensions. An AraBERT-based classification framework is further developed using imbalance-aware optimization, semi-supervised pseudo-labeling, and adaptive threshold calibration. Experimental results indicate that pseudo-label augmentation improves overall classification performance, suggesting that semi-supervised learning can support context-aware Arabic mental health classification. This study provides a context-aware annotation framework, a benchmark dataset, and an AraBERT-based baseline modeling pipeline for Arabic mental health NLP, thereby supporting future research on socially, culturally, and religiously grounded language technologies.