From Posts to Predictions: A User-Aware Framework for Faithful and Transparent Detection of Mental Health Risks on Social Media
Bibliographic record
Abstract
We propose a user-aware attention-based framework for early detection of mental health risks from social media posts.Our model combines DisorBERT, a mental health-adapted transformer encoder, with a user-level attention mechanism that produces transparent postlevel explanations.To assess whether these explanations are faithful, i.e., aligned with the model's true decision process, we apply adversarial training and quantify attention faithfulness using the AtteFa metric.Experiments on four eRisk tasks (depression, anorexia, self-harm, and pathological gambling) show that our model achieves competitive latencyweighted F1 scores while relying on a sparse subset of posts per user.We also evaluate attention robustness and conduct ablations, confirming the model's reliance on high-weighted posts.Our work extends prior explainability studies by integrating faithfulness assessment in a real-world high-stakes application.We argue that systems combining predictive accuracy with faithful and transparent explanations offer a promising path toward safe and trustworthy AI for mental health support.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".