41: Practical Approaches to Machine Learning for Mental Illness Detection on Social Media

Music City Center 

Social media provides a valuable avenue for mental health research, offering insights into conditions through user-generated content. Yet, the application of machine learning (ML) and deep learning (DL) models in this domain presents methodological challenges, including dataset representativeness, linguistic complexity, the need to distinguish multiple types of mental illness, and class imbalance. This project offers practical guidance to address these issues, focusing on best practices in data preprocessing, feature engineering, and model evaluation. The project introduces strategies for handling imbalanced datasets, optimizing hyperparameter tuning, and improving model transparency and reproducibility. Additionally, it demonstrates techniques for effectively differentiating various mental health conditions within social media data, ensuring that models capture their nuanced presentations. With real-world examples and step-by-step implementation, this project aims to provide tools to build more robust and interpretable ML/DL models for mental illness detection. These improvements contribute to the development of effective early detection and intervention tools in public health.

Machine Learning

Deep Learning

Mental Health

Social Media

NLP

Multi-Class Classification 

Mental Health Statistics Section