Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection
University of Pennsylvania（宾夕法尼亚大学）；School of Engineering and Applied Science（工程与应用科学学院）；New York University（纽约大学）；Center for Data Science（数据科学中心）；Georgia Institute of Technology（佐治亚理工学院）；College of Computing（计算机学院）；University of California, Berkeley（加州大学伯克利分校）；Department of EECS（电子工程与科学系）；Northeastern University（东北大学）；Khoury College of Computer Science（科里尔计算机科学学院）
Social media has become an important source for understanding mental health, providing researchers with a way to detect conditions like depression from user-generated posts. This tutorial provides practical guidance to address common challenges in applying machine learning and deep learning methods for mental health detection on these platforms. It focuses on strategies for working with diverse datasets, improving text preprocessing, and addressing issues such as imbalanced data and model evaluation. Real-world examples and step-by-step instructions demonstrate how to apply these techniques effectively, with an emphasis on transparency, reproducibility, and ethical considerations. By sharing these approaches, this tutorial aims to help researchers build more reliable and widely applicable models for mental health research, contributing to better tools for early detection and intervention.
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media
University of Pennsylvania（宾夕法尼亚大学）；School of Engineering and Applied Science（工程与应用科学学院）；New York University（纽约大学）；Center for Data Science（数据科学中心）；Georgia Institute of Technology（佐治亚理工学院）；College of Computing（计算学院）；University of California, Berkeley（加州大学伯克利分校）；Department of EECS（电子工程与科学系）；Northeastern University（东北大学）；Khoury College of Computer Science（计算机科学学院）
Quantifying Mental Health from Social Media with Neural User Embeddings
INESC-ID Lisboa（里斯本INESC-ID研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Qntfy（Qntfy公司）；Universidade Europeia（欧洲大学）；Northeastern University（东北大学）
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London（英国国立卫生研究院生物医学研究中心（位于南伦敦和莫兹利 NHS 基金会信托及伦敦国王学院））；University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）
Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges
Khoury College of Computer Science, Northeastern University（东北大学科瑞计算机学院）；University of California, Berkeley（加州大学伯克利分校）；Center for Data Science, New York University（纽约大学数据科学中心）；School of Engineering and Applied Science, University of Pennsylvania（宾夕法尼亚大学工程与应用科学学院）；Georgia Institute of Technology（佐治亚理工学院）
Adapting Deep Learning Methods for Mental Health Prediction on Social Media
Heidelberg Institute for Theoretical Studies gGmbH（海德堡理论研究所股份有限公司(Heidelberg Institute for Theoretical Studies gGmbH)（注：gGmbH通常译为“股份有限公司”，但该机构可能为非营利性质，此处按原文直译））
Investigating the Association Between Text-Based Indications of Foodborne Illness from Yelp Reviews and New York City Health Inspection Outcomes (2023)
Columbia University, Department of Computer Science（哥伦比亚大学计算机科学系）
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers
University of Alberta（阿尔伯塔大学）；Alberta Machine Intelligence Institute (Amii)（阿尔伯塔机器智能研究所）；LMU Munich（慕尼黑大学）；Carleton University（卡尔顿大学）；National Research Council Canada（加拿大国家研究委员会）
Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms
Georgia Institute of Technology（佐治亚理工学院）；Rowan University（罗文大学）；Zucker Hillside Hospital, Psychiatry Research（扎克山坡医院精神病学研究部）；Hofstra University（霍夫斯特拉大学）
PATIENT-Ψ: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
School of Computer Science, Carnegie Mellon University（卡内基梅隆大学计算机科学学院）；Department of Psychology, Princeton University（普林斯顿大学心理学系）；School of Social Work, University of Pittsburgh（匹兹堡大学社会工作学院）；Department of Psychiatry and Behavioral Sciences, Stanford University（斯坦福大学精神病学与行为科学系）
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study
Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health（坎贝尔家族心理健康研究所，成瘾与心理健康中心）；Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health（克伦比尔神经信息学中心，成瘾与心理健康中心）；University of Toronto（多伦多大学）；Princess Margaret Cancer Centre, University Health Network（玛格丽特公主癌症中心，大学健康网络）
