# Adapting Deep Learning Methods for Mental Health Prediction on Social Media

arXiv: https://arxiv.org/abs/2003.07634 · HTML: https://mlsanity.com/paper/2003.07634/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2020.

## Built on by (most cited first, 10 of 10)

- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice](https://mlsanity.com/paper/2501.00982/) (2025)
- [Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning](https://mlsanity.com/paper/2207.01012/) (2022)
- [Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN](https://mlsanity.com/paper/2302.02759/) (2023)
- [Emotion-based Modeling of Mental Disorders on Social Media](https://mlsanity.com/paper/2201.09451/) (2022)
- [Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks](https://mlsanity.com/paper/2306.16891/) (2023)
- [Deep Knowledge-Infusion For Explainable Depression Detection](https://mlsanity.com/paper/2409.02122/) (2024)
- [A Multimodal Framework for Depression Detection during Covid-19 via Harvesting Social Media: A Novel Dataset and Method](https://mlsanity.com/paper/2511.00424/) (2025)
- [Catch Me If You Can: Deceiving Stance Detection and Geotagging Models to Protect Privacy of Individuals on Twitter](https://mlsanity.com/paper/2207.11500/) (2022)
- [Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media](https://mlsanity.com/paper/2212.09839/) (2022)

## Abstract (arXiv)

Mental health poses a significant challenge for an individual's well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users' mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model's word-level attention weights.