Work

AI for multilingual mental health recognition via social media analysis:A systematic review

Year: 2026

Type: review

Abstract: Mental disorders affect approximately one billion individuals worldwide. Although conventional methods have the potential to diagnose these conditions, they suffer from societal stigma, insufficient facilities, and prolonged procedures. Social media provides an alternative approach that enables users to openly express their health issues, facilitating the use of artificial intelligence (AI) for early recognition. However, users’ communication styles vary owing to cultural and language differences. To address this problem, we present a systematic review of 94 articles from 2018 to 2025 that implemented AI to identify mental health-related issues through social media, focusing on studies analyzing languages other than English. Our review indicates that research has concentrated on three mental health conditions: depression, suicidal ideation, and anxiety, while conditions such as stress, bipolar, Obsessive-Compulsive Disorder (OCD), anorexia, Post-Traumatic Stress Disorder (PTSD), and Attention Deficit Hyperactivity Disorder (ADHD) remain underexplored. The top three languages in research are Chinese ( n =21), Arabic ( n =20), Thai ( n =12), and Bengali ( n =12), with additional studies in Indonesian, Spanish, Korean, Roman Urdu, Filipino, Russian, Sinhala, Brazilian Portuguese, German, Dutch, Japanese, French, and Malay. The methodologies have progressed from machine learning (ML) to deep learning (DL) approaches and transformer-based models (BERT, RoBERTa, DistilBERT). Challenges include data sparsity, class imbalance, annotation noise, culture-specificity, and the trade-off between the scalability of labeling based on data collection and label quality. Future research should include multilingual datasets, more attention to underexplored areas of mental health conditions, explainable AI, and domain-specific language models.

Source: Array

DOI: 10.1016/j.array.2026.101373

Authors: Alfiyah Qothrunnada Zulfa, Sunu Wibirama, Syukron Abu Ishaq Alfarozi, Sri Kusrohmaniah

Institutions: Yogyakarta State University, Universitas Gadjah Mada

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDGs: Good health and well-being, Reduced inequalities

Open Access status: gold

Estimated APC paid: $1,680