Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media

--The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERT a, DistilBERT, ALBERT, and ELECTRA) against Long Short-T erm Memory (LSTM) based approaches using different text embedding techniques for mental health disorder classification on Reddit. We construct a large annotated dataset, validating its reliability through statistical judgmental analysis and topic modeling. Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches. RoBERT a achieved the highest classification performance, with a 99.54% F1 score on the hold-out test set and a 96.05% F1 score on the external test set. Notably, LSTM models augmented with BERT embeddings proved highly competitive, achieving F1 scores exceeding 94% on the external dataset while requiring significantly fewer computational resources. We discuss the implications for clinical applications and digital mental health interventions, offering insights into the capabilities and limitations of state-of-the-art NLP methodologies in mental disorder detection. Mental health conditions such as depression, anxiety, and schizophrenia remain prevalent global health challenges. According to the World Health Organization (WHO), one in eight people will experience a mental illness during their lifetime [1], making early detection and intervention critical. Untreated mental health conditions often lead to serious functional impairment, reduced quality of life, and increased mortality risk [2]. These disorders' significant societal and economic impact underscores the need for effective monitoring and treatment tools [3].