Computational mental health research has predominantly centered on English-speaking populations, leaving Arabic-language discourse comparatively under-examined. We present an exploratory computational study of 8,147 tweets from 607 users classified by a GPT-4.1 personal-disclosure pipeline as likely lived-experience authors in three condition-specific Arabic-language X (formerly Twitter) Communities. We focus on discourse related to borderline personality disorder (BPD), bipolar disorder, and ADHD, and characterize community-associated linguistic patterns using a multi-domain cultural keyword framework. The results suggest that in this corpus, Bipolar tweets contain more religious and medical vocabulary, BPD tweets contain more relational, identity, and emotional-distress vocabulary, and ADHD tweets more often focus on practical symptoms and medication management. We treat these patterns as hypothesis-generating rather than confirmatory because the corpus is imbalanced across conditions, some subcorpora are temporally concentrated, and the keyword framework is an initial operationalization rather than a validated measurement instrument. The paper contributes a reusable LLM-assisted personal-disclosure pipeline and an exploratory cultural keyword framework for Arabic mental health discourse.

Language-based schizophrenia detection has focused predominantly on English, leaving Arabic underrepresented in mental health NLP. We introduce an Arabic schizophrenia social media dataset (228 users, 228 controls; Cohen’s κ=0.76) and evaluate cross-lingual and cross-genre transfer across five primary settings and four reversed direction counterparts using XLM-RoBERTa-base, mBERT, and MARBERT. In-domain Arabic supervision yields the strongest performance, with MARBERT reaching 0.7706 macro-F1. In contrast, no primary transfer condition satisfies our criterion for robust transfer (macro-F1 > 0.50 and consistent performance across all three encoders). Reversed direction experiments suggest that genre transfer is directional: social to clinical transfer partially succeeds in the Arabic-only setting, whereas clinical to social transfer collapses. Results from translated clinical data further indicate that translation-based evaluation can introduce artifacts specific to encoder and direction. Overall, the findings support the need for native target language and target genre resources for reliable multilingual mental health evaluation.

We present a multi-resolution framework for analyzing longitudinal Arabic mental-health discourse drawn from condition-specific Arabic-language X (formerly Twitter) communities. We model each user’s posting history as a calendar-time semantic trajectory and apply the Discrete Wavelet Transform (DWT) along the temporal axis to separate semantic variation occurring over finer and broader time scales. To interpret these temporal components linguistically, we introduce semantic gain, which measures how strongly corpus-derived phrases become aligned with the reconstructed semantic trajectory when information from a given temporal scale is introduced. We evaluate the framework on longitudinal trajectories from BPD-related and bipolar-related X communities using independent temporal-change measures, chronological permutation tests, cross-scale phrase distinctiveness, and matched high- versus low- gain events. Finer-scale DWT components align most strongly with local adjacent semantic change, whereas broader-scale components show greater correspondence with distributional change measured by Maximum Mean Discrepancy (MMD). Our findings suggest that longitudinal mental-health discourse on X exhibits multiscale semantic structure, with different forms of linguistic change emerging over different temporal horizons. Combining wavelet decomposition with phrase-based semantic interpretation provides a principled way to characterize both the temporal scale and linguistic content of discourse change.

The prevalence of chronic stress represents a major public health concern, yet automated detection of vulnerable individuals remains limited. Social media platforms like X (formerly Twitter) serve as important venues for people to share their experiences openly. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for the automatic detection of self-reported chronic stress in English tweets. We investigate whether continual pretraining on clinically related conditions, such as depression, anxiety, and PTSD, which have a high comorbidity with chronic stress, improves stress detection compared to general language models. We continually pretrained RoBERTa on the Stress-SMHD corpus, a subset of Self-reported Mental Health Diagnoses focused on stress-related conditions, consisting of 108 million words from users with self-reported diagnoses of depression, anxiety, and PTSD. Then, we fine-tuned on the SMM4H 2022 Shared Task 8. StressRoBERTa achieves 82% F1, which outperforms the best shared task system (79% F1) by 3 percentage points. Our results demonstrate that focused cross-condition transfer learning from stress-related disorders provides stronger representations than general mental health training. To validate cross-condition generalization, we also fine-tuned the model on the Dreaddit. Our result of 81% F1 further demonstrates the transfer from clinical mental health contexts to situational stress discussions.

Summarizing medical conversations is one of the tasks proposed by MEDIQA-Chat to promote research on automatic clinical note generation from doctor-patient conversations. In this paper, we present our submission to this task using fine-tuned language models, including T5, BART and BioGPT models. The fine-tuned models are evaluated using ensemble metrics including ROUGE, BERTScore andBLEURT. Among the fine-tuned models, Flan-T5 achieved the highest aggregated score for dialogue summarization.

Schizophrenia is one of the most disabling mental health conditions to live with. Approximately one percent of the population has schizophrenia which makes it fairly common, and it affects many people and their families. Patients with schizophrenia suffer different symptoms: formal thought disorder (FTD), delusions, and emotional flatness. In this paper, we quantitatively and qualitatively analyze the language of patients with schizophrenia measuring various linguistic features in two modalities: speech and written text. We examine the following features: coherence and cohesion of thoughts, emotions, specificity, level of commit- ted belief (LCB), and personality traits. Our results show that patients with schizophrenia score high in fear and neuroticism compared to healthy controls. In addition, they are more committed to their beliefs, and their writing lacks details. They score lower in most of the linguistic features of cohesion with significant p-values.