# DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment

arXiv: https://arxiv.org/abs/2501.14985 · HTML: https://mlsanity.com/paper/2501.14985/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 24 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Parameterized Explainer for Graph Neural Network](https://mlsanity.com/paper/2011.04573/) (2020)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Attention in Natural Language Processing](https://mlsanity.com/paper/1902.02181/) (2019)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018)
- [Understanding Convolutional Neural Networks for Text Classification](https://mlsanity.com/paper/1809.08037/) (2018)
- [The Role of Explainability in Assuring Safety of Machine Learning in Healthcare](https://mlsanity.com/paper/2109.00520/) (2021)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings](https://mlsanity.com/paper/2209.07494/) (2022)
- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [Heterogeneous network approach to predict individuals' mental health](https://mlsanity.com/paper/1906.04346/) (2019)

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

- [AI Models for Depressive Disorder Detection and Diagnosis: A Review](https://mlsanity.com/paper/2508.12022/) (2025)
- [AttentionDep: Domain-Aware Attention for Explainable Depression Severity Assessment](https://mlsanity.com/paper/2510.00706/) (2025)
- [Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity](https://mlsanity.com/paper/2609.39049/) (2026)

## Abstract (arXiv)

In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, mathbbX (formerly Twitter), and Reddit for mental health assessments. We propose a domain knowledge-infused residual attention model called DepressionX for explainable depression severity detection. Existing deep learning models on this problem have shown considerable performance, but they often lack transparency in their decision-making processes. In healthcare, where decisions are critical, the need for explainability is crucial. In our model, we address the critical gap by focusing on the explainability of depression severity detection while aiming for a high performance accuracy. In addition to being explainable, our model consistently outperforms the state-of-the-art models by over 7% in terms of F1 score on balanced as well as imbalanced datasets. Our ultimate goal is to establish a foundation for trustworthy and comprehensible analysis of mental disorders via social media.