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    <abstract>Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance. Specifically, we apply federatedlearning (FL) and Differentially Private FL for two widely-studied mental health prediction tasks: depression detection on X (Twitter) and suicide crisis detection on Reddit. We simulate realistic data-sharing scenarios by treating each user as a client in a non-IID setting, evaluating across different client fractions, aggregation strategies, and privacy budgets. While FL achieves comparable performance to centralized training (centralized \ensuremath\mathitF 1 = 85.63; best FL model \ensuremath\mathitF 1 = 83.16) on depression identification, we find that Differentially Private FL has a large performance-privacy trade-off (up to \ensuremath\mathitF 1 = 27.01 drop) even with low levels of noise (𝜖 = 50). This is due to the distortion of highly informative yet sparse mental health linguistic markers related to mental health, like health topics and emotion words. This research empirically demonstrates the potential and limitations of current privacy preservation techniques for mental health inference tasks.</abstract>
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%0 Conference Proceedings
%T FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data
%A Abdelkadir, Nuredin Ali
%A Ratnam, Anjali
%A Talat, Zeerak
%A Chancellor, Stevie
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F abdelkadir-etal-2026-fedmental
%X Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance. Specifically, we apply federatedlearning (FL) and Differentially Private FL for two widely-studied mental health prediction tasks: depression detection on X (Twitter) and suicide crisis detection on Reddit. We simulate realistic data-sharing scenarios by treating each user as a client in a non-IID setting, evaluating across different client fractions, aggregation strategies, and privacy budgets. While FL achieves comparable performance to centralized training (centralized \ensuremath\mathitF 1 = 85.63; best FL model \ensuremath\mathitF 1 = 83.16) on depression identification, we find that Differentially Private FL has a large performance-privacy trade-off (up to \ensuremath\mathitF 1 = 27.01 drop) even with low levels of noise (𝜖 = 50). This is due to the distortion of highly informative yet sparse mental health linguistic markers related to mental health, like health topics and emotion words. This research empirically demonstrates the potential and limitations of current privacy preservation techniques for mental health inference tasks.
%U https://aclanthology.org/2026.acl-long.1899/
%P 40929-40944

Markdown (Informal)

[FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data](https://aclanthology.org/2026.acl-long.1899/) (Abdelkadir et al., ACL 2026)

• FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data (Abdelkadir et al., ACL 2026)

ACL

• Nuredin Ali Abdelkadir, Anjali Ratnam, Zeerak Talat, and Stevie Chancellor. 2026. FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 40929–40944, San Diego, California, United States. Association for Computational Linguistics.