# Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings

arXiv: https://arxiv.org/abs/2506.06616 · HTML: https://mlsanity.com/paper/2506.06616/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Abstractive Summarization of Reddit Posts with Multi-level Memory Networks](https://mlsanity.com/paper/1811.00783/) (2018)
- [Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data](https://mlsanity.com/paper/2307.14385/) (2023)
- [Your Mixture-of-Experts LLM Is Secretly an Embedding Model For Free](https://mlsanity.com/paper/2410.10814/) (2024)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Understanding and Measuring Psychological Stress using Social Media](https://mlsanity.com/paper/1811.07430/) (2018)
- [DepressionEmo: A novel dataset for multilabel classification of depression emotions](https://mlsanity.com/paper/2401.04655/) (2024)

## Built on by (most cited first, 6 of 6)

- [Assessing the Quality of Mental Health Support in LLM Responses through Multi-Attribute Human Evaluation](https://mlsanity.com/paper/2601.18630/) (2026)
- [AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?](https://mlsanity.com/paper/2603.19574/) (2026)
- [Improving Topic Modeling of Social Media Short Texts with Rephrasing: A Case Study of COVID-19 Related Tweets](https://mlsanity.com/paper/2510.18908/) (2025)
- [Menta: A Small Language Model for On-Device Mental Health Prediction](https://mlsanity.com/paper/2512.02716/) (2025)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)
- [Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity](https://mlsanity.com/paper/2609.39049/) (2026)

## Abstract (arXiv)

Accurate and interpretable detection of depressive language in social media can support early identification of mental health conditions and inform timely interventions. In this paper, we investigate the use of large language models (LLMs) and traditional machine learning classifiers for three social media-based mental health prediction tasks: binary depression classification, depression severity classification, and differential diagnosis among depression, PTSD, and anxiety. We compare zero-shot LLMs with supervised classifiers trained on conventional text embeddings, psycholinguistic features, and embeddings derived from LLM-generated mental health summaries. Across multiple publicly available social media text datasets and five-fold cross-validation experiments, we find that zero-shot LLMs exhibit strong performance and generalization in binary depression classification, but struggle with fine-grained severity prediction. In contrast, supervised models trained on LLM summary embeddings often achieve more accurate and consistent performance, particularly for multi-class and ordinal classification tasks. These findings highlight both the strengths and limitations of current LLMs for mental health prediction and suggest that using LLMs as semantic interpreters, rather than solely as end-to-end classifiers, may provide a promising direction for building more effective and interpretable mental health assessment systems.