Understanding the Sociocultural Dimensions of Mental Health Discourse in Arabic-Language X Communities

2026-06-06

AI Summary

Arabic-language mental health discourse has long been underrepresented in computational research, with limited systematic understanding of the linguistic and cultural characteristics specific to communities discussing particular psychiatric conditions. This study addresses this gap by analyzing 8,147 Arabic tweets from 607 users on X (formerly Twitter), focusing on three diagnostic groups: borderline personality disorder, bipolar disorder, and attention-deficit/hyperactivity disorder (ADHD). We propose the first reusable LLM-assisted pipeline for identifying personal disclosures in Arabic mental health discourse and develop a culturally grounded keyword analysis framework, leveraging GPT-4 for multidimensional linguistic feature extraction and cross-group comparison. Our findings reveal distinct discursive patterns: bipolar disorder posts frequently incorporate religious and medical terminology; borderline personality disorder discourse centers on relational dynamics, identity struggles, and emotional distress; and ADHD-related content predominantly addresses symptom expression and medication management.

Abstract

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.

Problem

• Arabic-language mental health discourse
• sociocultural dimensions
• borderline personality disorder
• bipolar disorder
• ADHD

Innovation

• LLM-assisted pipeline
• cultural keyword framework
• Arabic mental health discourse
• computational mental health
• personal-disclosure detection