Leveraging Social Media and AI for Early Community Mental Health Support
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".