Advancing Mental Disorder Detection: Evaluating Transformer and LSTM-Based Models on Social Media

Author

Khalid Hasan, Missouri State UniversityFollow

Date of Graduation

Summer 2025

Degree

Master of Science in Computer Science

Department

Computer Science

Committee Chair

Jamil Saquer

Abstract

Mental disorders such as suicidal ideation, bipolar disorder, and depression are common, and early detection is crucial for effective intervention. Social media has emerged as a bountiful source of mental illness indicators since many individuals express their psychological state over the internet. This thesis explores social media text usage for identifying mental disorders based on advanced Natural Language Processing (NLP) techniques. The scope spans four principal tasks: early prediction of suicidal ideation; bipolar disorder-related language classification; binary classification distinguishing between individuals with mental disorders and healthy controls; and multi-class diagnosis of six individual conditions (Attention-deficit/hyperactivity disorder (ADHD), anxiety, bipolar disorder, depression, Complex Post-Traumatic Stress Disorder (CPTSD), and schizophrenia) versus a control group. The performance of recent Transformer-based models (BERT, RoBERTa, DistilBERT, ALBERT, ELECTRA) and Long Short-Term Memory (LSTM) networks augmented with attentional and contextual embedding is investigated, evaluating their efficacy across these tasks. Experimental results show that Transformer models consistently display excellent classification performance, whereas LSTM-based models offer competitive accuracy with reduced computational expense when integrated with contextual embedding. Above all, light Transformer versions (e.g., DistilBERT) achieve near that of the large models’ accuracy while being significantly more efficient, indicating their feasibility for deployment in real-time or resource-limited settings. Model robustness regarding identifying general mental disorder is further assessed, and it is concluded that top-performing models exhibit robust performance under various data conditions. The main contributions of this work include the construction of annotated social media corpora, an exhaustive benchmark of state-of-the-art NLP models on mental health prediction, and new findings on models’ efficiency-performance-generalizability trade-offs. These findings advance the development of feasible NLP-based early intervention monitoring tools for mental health.

Keywords

mental health, suicidal ideation, Reddit, NLP, deep learning, BERT, LSTM, social media mining

Subject Categories

Artificial Intelligence and Robotics | Categorical Data Analysis | Computational Engineering | Data Science | Health Psychology | Personality and Social Contexts | Social Psychology | Statistical Methodology | Vital and Health Statistics

Copyright

© Khalid Hasan

Recommended Citation

Hasan, Khalid, "Advancing Mental Disorder Detection: Evaluating Transformer and LSTM-Based Models on Social Media" (2025). Graduate Theses/Dissertations. 4096.
https://bearworks.missouristate.edu/theses/4096