ORIGINAL RESEARCH article
Front. Digit. Health
Sec. Health Informatics
This article is part of the Research TopicEthical Considerations of Large Language Models: Challenges and Best PracticesView all 8 articles
DP-CARE: A Differentially Private Classifier for Mental Health Analysis in Social Media Posts
Provisionally accepted
Dimitris Karpontinis*
Efstathia Soufleri- Archimedes, Athena Research Center, Greece, Athens, Greece
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Mental health NLP models are increasingly used to detect psychological states such as stress and depression from user-generated social media content. While transformer-based models like MentalBERT Ji et al. (2021) have demonstrated strong performance in this domain, they are typically trained on sensitive data, raising concerns about memorization and unintended disclosure of personally identifiable information. In this work, we introduce DP-CARE, a simple yet effective privacy-preserving framework that attaches a lightweight classifier to a frozen, domain-specific encoder and trains it using Differentially Private AdamW (DP-AdamW). DP-CARE mitigates privacy risks while maintaining computational efficiency and high predictive performance. We evaluate our method on the Dreaddit dataset for stress detection and show that it achieves competitive results (F1 = 78.08%, Recall = 88.67%) under a privacy budget of ε ≈3. These findings demonstrate that lightweight, differentially private fine-tuning provides a practical and ethical approach for deploying NLP systems in privacy-sensitive domains.
Keywords: Mental Health, LLM, Stress detection, Privacy, Differential privacy
Received: 25 Sep 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Karpontinis and Soufleri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dimitris Karpontinis
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