# Leveraging Social Media and AI for Early Community Mental Health Support

## TL;DR

This paper presents a framework using AI to detect mental health concerns in social media posts and provide personalized support resources to users.

## Contribution

The novel contribution is a system that connects mental health symptom detection with the generation of tailored resources using natural language processing and large language models.

## Key findings

- DistilBERT achieved the highest performance with an AUC of 0.916 and an F1 score of 0.762.
- The LLM generated personalized mental health resources based on high-confidence predictions from the models.
- The framework aims to lower barriers to mental healthcare by providing scalable community-based outreach.

## Abstract

Background

Mental health conditions have become a leading cause of disability worldwide, yet stigma, financial barriers, and limited access to care impede effective treatment. Amid these challenges, more people are turning to online platforms like Reddit to express psychological distress and seek informal support. However, these platforms often lack mechanisms to guide users toward professional care. This study explores a community-facing framework that leverages natural language processing and large language models (LLMs) to detect mental health concerns in social media posts and generate personalized support options.

Methods

We trained and evaluated multiple machine learning classifiers, including Logistic Regression, Random Forest, XGBoost, and DistilBERT, using the Reddit SuicideWatch and Mental Health Collection datasets for multilabel classification of mental health conditions, including depression, anxiety, bipolar disorder, and suicidal ideation. High-confidence predictions from these models were then used to prompt Llama 3.1 8B Turbo LLM to generate personalized mental health resources.

Results

Among the models, DistilBERT achieved the highest performance, with an area under the receiver operating characteristic curve of 0.916 (95% CI: 0.912-0.921), an F1 score of 0.762 (95% CI: 0.753-0.771), and an accuracy of 0.761 (95% CI: 0.752-0.770). Using these predictions, the LLM generated tailored resources matched to the identified mental health concerns.

Conclusion

By connecting symptom detection with resource generation, this framework aims to lower common barriers to mental healthcare, especially for individuals hesitant to seek traditional support. Instead of viewing classification as an endpoint, our approach shows how detection can lead to intervention. Linking symptom recognition with tailored resource creation, this work underscores AI’s potential to enable scalable, community-based mental health outreach that complements traditional care delivered by licensed mental health professionals in clinical settings.