# Emotion-based Modeling of Mental Disorders on Social Media

arXiv: https://arxiv.org/abs/2201.09451 · HTML: https://mlsanity.com/paper/2201.09451/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://mlsanity.com/paper/2003.07634/) (2020)
- [Not Just Depressed: Bipolar Disorder Prediction on Reddit](https://mlsanity.com/paper/1811.04655/) (2018)

## Built on by (most cited first, 4 of 4)

- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [Concepts and Experiments on Psychoanalysis Driven Computing](https://mlsanity.com/paper/2210.00850/) (2022)
- [Mental Disorder Classification via Temporal Representation of Text](https://mlsanity.com/paper/2406.15470/) (2024)

## Abstract (arXiv)

According to the World Health Organization (WHO), one in four people will be affected by mental disorders at some point in their lives. However, in many parts of the world, patients do not actively seek professional diagnosis because of stigma attached to mental illness, ignorance of mental health and its associated symptoms. In this paper, we propose a model for passively detecting mental disorders using conversations on Reddit. Specifically, we focus on a subset of mental disorders that are characterized by distinct emotional patterns (henceforth called emotional disorders): major depressive, anxiety, and bipolar disorders. Through passive (i.e., unprompted) detection, we can encourage patients to seek diagnosis and treatment for mental disorders. Our proposed model is different from other work in this area in that our model is based entirely on the emotional states, and the transition between these states of users on Reddit, whereas prior work is typically based on content-based representations (e.g., n-grams, language model embeddings, etc). We show that content-based representation is affected by domain and topic bias and thus does not generalize, while our model, on the other hand, suppresses topic-specific information and thus generalizes well across different topics and times. We conduct experiments on our model's ability to detect different emotional disorders and on the generalizability of our model. Our experiments show that while our model performs comparably to content-based models, such as BERT, it generalizes much better across time and topic.