# Depression Detection on Social Media with Large Language Models

arXiv: https://arxiv.org/abs/2403.10750 · HTML: https://mlsanity.com/paper/2403.10750/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [S3: Social-network Simulation System with Large Language Model-Empowered Agents](https://mlsanity.com/paper/2307.14984/) (2023)
- [The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development](https://mlsanity.com/paper/2302.07080/) (2023)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Towards Interpretable Mental Health Analysis with Large Language Models](https://mlsanity.com/paper/2304.03347/) (2023)
- [Stance Detection with Collaborative Role-Infused LLM-Based Agents](https://mlsanity.com/paper/2310.10467/) (2023)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires](https://mlsanity.com/paper/2204.10432/) (2022)
- [Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework](https://mlsanity.com/paper/2011.06149/) (2020)
- [Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health](https://mlsanity.com/paper/2304.10447/) (2023)
- [Towards Preemptive Detection of Depression and Anxiety in Twitter](https://mlsanity.com/paper/2011.05249/) (2020)
- [Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://mlsanity.com/paper/2302.12044/) (2023)
- [An Assessment on Comprehending Mental Health through Large Language Models](https://mlsanity.com/paper/2401.04592/) (2024)

## Built on by (most cited first, 20 of 20)

- [PrefillOnly: An Inference Engine for Prefill-only Workloads in Large Language Model Applications](https://mlsanity.com/paper/2505.07203/) (2025)
- [RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning](https://mlsanity.com/paper/2505.14140/) (2025)
- [HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction](https://mlsanity.com/paper/2410.09112/) (2024)
- [Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels](https://mlsanity.com/paper/2410.04501/) (2024)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge](https://mlsanity.com/paper/2505.22863/) (2025)
- [AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention Cache](https://mlsanity.com/paper/2510.25979/) (2025)
- [Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning](https://mlsanity.com/paper/2606.10796/) (2026)
- [LLM Assistance for Pediatric Depression](https://mlsanity.com/paper/2501.17510/) (2025)
- [Learning Evidence of Depression Symptoms via Prompt Induction](https://mlsanity.com/paper/2604.24376/) (2026)
- [MMHBench: A Multi-Perspective Benchmark for Mental Health Understanding in Long-Form Videos](https://mlsanity.com/paper/2607.27895/) (2026)
- [Chatting Up Attachment: Using LLMs to Predict Adult Bonds](https://mlsanity.com/paper/2409.00347/) (2024)
- [HALO: Memory-Centric Heterogeneous Accelerator with 2.5D Integration for Low-Batch LLM Inference](https://mlsanity.com/paper/2510.02675/) (2025)
- [A Machine Learning Approach for Detection of Mental Health Conditions and Cyberbullying from Social Media](https://mlsanity.com/paper/2511.20001/) (2025)
- [Automated Data Enrichment using Confidence-Aware Fine-Grained Debate among Open-Source LLMs for Mental Health and Online Safety](https://mlsanity.com/paper/2512.06227/) (2025)
- [Can We Trust LLMs for Mental Health Screening? Consistency, ASR Robustness, and Evidence Faithfulness](https://mlsanity.com/paper/2605.09634/) (2026)
- [EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimation](https://mlsanity.com/paper/2605.22286/) (2026)
- [Cognivia: A Cognitive Behavioral Therapy Copilot for Evidence-Based Mental Healthcare](https://mlsanity.com/paper/2607.25681/) (2026)
- [Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition](https://mlsanity.com/paper/2609.01833/) (2026)
- [LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues](https://mlsanity.com/paper/2609.03507/) (2026)

## Abstract (arXiv)

Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes. Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual requirement for high accuracy and model explainability. To address this, we propose DORIS, a framework that leverages Large Language Models (LLMs). To integrate medical knowledge, DORIS utilizes LLMs to annotate user texts against established medical diagnostic criteria and to summarize historical posts into temporal mood courses. These medically-informed features are then used to train an accurate Gradient Boosting Tree (GBT) classifier. Explainability is achieved by generating justifications for predictions based on the LLM-derived symptom annotations and mood course analyses. Extensive experimental results validate the effectiveness as well as interpretability of our method, highlighting its potential as a supportive clinical tool.