New Arabic Language Model Improves Detection of Mental Health Disorders in Social Media Text
Researchers have developed MentalMARBERT, a domain-adapted Arabic language model designed to classify mental health disorders from social media text, achieving a macro-F1 score of 0.861 and accuracy of 0.877. The study addresses a significant gap in Arabic NLP, where multi-class mental health disorder classification has been understudied compared to English-language systems. The work is notable for introducing a new annotated Arabic mental health dataset of over 50,000 tweets and demonstrating that hierarchical classification architectures outperform single-stage approaches.
A research team has proposed a two-phase framework called MentalMARBERT for detecting mental health disorders in Arabic social media text, targeting challenges including dialectal variation, informal language, and class imbalance. In the first phase, three Arabic pre-trained language models — AraBERT, CAMeLBERT, and MARBERT — underwent Domain-Adaptive and Task-Adaptive Pretraining (DAPT and TAPT) using a large corpus of unlabeled Arabic mental health tweets, with MARBERT emerging as the most effective backbone. In the second phase, the adapted model was evaluated across four configurations combining single-stage and hierarchical two-stage classification with full fine-tuning and Low-Rank Adaptation (LoRA). To support the research, the team constructed a novel annotated dataset of 50,670 tweets spanning six mental health categories, with strong inter-annotator agreement (Krippendorff's Alpha = 0.733). The best-performing configuration — hierarchical two-stage classification with full fine-tuning — achieved a macro-F1 of 0.861 and accuracy of 0.877, representing statistically significant improvements over baseline models. The findings suggest that domain-specific adaptive pretraining and hierarchical classification are effective strategies for Arabic mental health NLP tasks.
What's missing
The dataset is drawn exclusively from Twitter, which may limit generalizability to other platforms or offline populations. The paper does not report external validation on independent datasets outside the constructed corpus, leaving open questions about robustness across different Arabic dialects and demographic groups.
What different sources said
- arXiv cs.CLCenter
MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
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