Abstract Number:
1484
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Zhongyan Wang (1), Zhanyi Ding (1), Yexin Tian (2), Jianglai Dai (3), Xiaorui Shen (4), Yeyubei Zhang (5), Yunchong Liu (5), Yuchen Cao (4)
Institutions:
(1) New York University, N/A, (2) Georgia Institute of Technology, N/A, (3) UC Berkeley, N/A, (4) Northeastern University, N/A, (5) University of Pennsylvania, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
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.
Keywords:
Machine Learning|Deep Learning|Mental Health|Social Media|NLP| Multi-Class Classification
Sponsors:
Mental Health Statistics Section
Tracks:
Big data/machine learning
Can this be considered for alternate subtype?
No
Are you interested in volunteering to serve as a session chair?
No
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand that JSM participants must register and pay the appropriate registration fee by June 3, 2025. The registration fee is non-refundable.
I understand