MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
A new study proposes MentalMARBERT, a domain-adapted version of MARBERT, for detecting mental health disorders from Arabic social media text. Using a two-phase framework with adaptive pre-training and hierarchical fine-tuning, the model achieves state-of-the-art performance with 0.861 macro-F1 and 0.877 accuracy on a novel dataset of 50,670 tweets across six categories.
[2606.12649] MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
[Submitted on 10 Jun 2026]
Title:MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection
View a PDF of the paper titled MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection, by Fatimah Almalki and 3 other authors
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Abstract:Detecting mental health disorders from Arabic social media text remains challenging due to dialectal variation, informal language, limited high-quality annotated resources, and severe class imbalance. While English mental health natural language processing (NLP) has progressed substantially, Arabic multi-class disorder classification remains insufficiently studied. This study proposes a two-phase framework for Arabic mental health text classification. In phase 1, three Arabic pre-trained language models, AraBERT, CAMeLBERT, and MARBERT, undergo Domain-Adaptive and Task-Adaptive Pretraining (DAPT and TAPT) using a large-scale corpus of unlabeled Arabic mental health tweets. The adapted models are evaluated under a unified protocol to identify the most effective backbone model. In phase 2, the selected model is assessed across four configurations combining single-stage and hierarchical two-stage classification architectures with full fine-tuning and Low-Rank Adaptation (LoRA). To support this study, we constructed a novel annotated Arabic mental health dataset comprising 50,670 tweets across six categories, with strong inter annotator agreement (Krippendorff's Alpha = 0.733, average pairwise agreement = 0.797). Experimental results show that the domain-adapted MARBERT (MentalMARBERT) achieves statistically significant improvements over baseline models in both accuracy and macro-F1. The hierarchical two-stage architecture combined with full fine-tuning achieves the best overall performance, reaching a macro-F1 of 0.861 and an accuracy of 0.877. These findings demonstrate the effectiveness of domain-specific adaptive pretraining and hierarchical classification for Arabic mental health disorder detection.
Comments: 17 pages, 5 figures, 13 tables
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.12649 [cs.CL]
(or arXiv:2606.12649v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.12649
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Fatimah Almalki [view email] [v1] Wed, 10 Jun 2026 20:20:41 UTC (615 KB)
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