mlsanityData to 7 Oct 2026

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

arXiv 2502.04342 ↗First posted 3 Feb 2025cs.CL cs.AI cs.LGcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 32 references that are in the map. First posted on arXiv 3 Feb 2025.

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11 of 32 references.

References · 11

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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Datacited by 20
  2. 2025Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Mediacited by 2
  3. 2025On Theoretical Limits of Learning with Label Differential Privacycited by 0