Tutorial on Using Machine Learning and Deep Learning Models for Mental Illness Detection

TL;DR

This tutorial offers practical guidance on applying machine learning and deep learning techniques to detect mental health issues from social media data, emphasizing data handling, model evaluation, and ethical considerations.

Contribution

It provides step-by-step instructions and strategies for overcoming common challenges in mental health detection models using social media data.

Findings

Effective data preprocessing improves model accuracy.

Addressing data imbalance enhances detection reliability.

Transparent evaluation methods are crucial for trustworthy models.

Abstract

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.