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

arXiv: https://arxiv.org/abs/2502.04342 · HTML: https://mlsanity.com/paper/2502.04342/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Decoding by Contrasting Knowledge: Enhancing LLMs' Confidence on Edited Facts](https://mlsanity.com/paper/2405.11613/) (2024)
- [Minimax Optimal Q Learning with Nearest Neighbors](https://mlsanity.com/paper/2308.01490/) (2023)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [Simultaneously detecting spatiotemporal changes with penalized Poisson regression models](https://mlsanity.com/paper/2405.06613/) (2024)
- [Machine Learning Approaches for Mental Illness Detection on Social Media: A Systematic Review of Biases and Methodological Challenges](https://mlsanity.com/paper/2410.16204/) (2024)
- [A Systematic Review of Machine Learning Approaches for Detecting Deceptive Activities on Social Media: Methods, Challenges, and Biases](https://mlsanity.com/paper/2410.20293/) (2024)
- [An Improved Dung Beetle Optimizer for Random Forest Optimization](https://mlsanity.com/paper/2411.17738/) (2024)

## Built on by (most cited first, 3 of 3)

- [Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Data](https://mlsanity.com/paper/2505.22521/) (2025)
- [Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media](https://mlsanity.com/paper/2503.01082/) (2025)
- [On Theoretical Limits of Learning with Label Differential Privacy](https://mlsanity.com/paper/2502.14309/) (2025)

## Abstract (arXiv)

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.