# Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

arXiv: https://arxiv.org/abs/2503.01442 · HTML: https://mlsanity.com/paper/2503.01442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://mlsanity.com/paper/2309.13567/) (2023)
- [A Computational Framework for Behavioral Assessment of LLM Therapists](https://mlsanity.com/paper/2401.00820/) (2024)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering](https://mlsanity.com/paper/2402.05127/) (2024)
- [Identifying pandemic-related stress factors from social-media posts -- effects on students and young-adults](https://mlsanity.com/paper/2012.00333/) (2020)

## Built on by (most cited first, 2 of 2)

- [Using LLM-as-a-Judge/Jury to Advance Scalable, Clinically-Validated Safety Evaluations of Model Responses to Users Demonstrating Psychosis](https://mlsanity.com/paper/2604.02359/) (2026)
- [Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration](https://mlsanity.com/paper/2607.23538/) (2026)

## Abstract (arXiv)

Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.