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    <abstract>The prevalence of chronic stress represents a major public health concern, yet automated detection of vulnerable individuals remains limited. Social media platforms like X (formerly Twitter) serve as important venues for people to share their experiences openly. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for the automatic detection of self-reported chronic stress in English tweets. We investigate whether continual pretraining on clinically related conditions, such as depression, anxiety, and PTSD, which have a high comorbidity with chronic stress, improves stress detection compared to general language models. We continually pretrained RoBERTa on the Stress-SMHD corpus, a subset of Self-reported Mental Health Diagnoses focused on stress-related conditions, consisting of 108 million words from users with self-reported diagnoses of depression, anxiety, and PTSD. Then, we fine-tuned on the SMM4H 2022 Shared Task 8. StressRoBERTa achieves 82% F1, which outperforms the best shared task system (79% F1) by 3 percentage points. Our results demonstrate that focused cross-condition transfer learning from stress-related disorders provides stronger representations than general mental health training. To validate cross-condition generalization, we also fine-tuned the model on the Dreaddit. Our result of 81% F1 further demonstrates the transfer from clinical mental health contexts to situational stress discussions.</abstract>
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%0 Conference Proceedings
%T StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection
%A Alqahtani, Amal Abdullah
%A Kayi, Efsun
%A Diab, Mona T.
%Y Danilova, Vera
%Y Kurfalı, Murathan
%Y Söderfeldt, Ylva
%Y Reed, Julia
%Y Burchell, Andrew
%S Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-367-8
%F alqahtani-etal-2026-stressroberta
%X The prevalence of chronic stress represents a major public health concern, yet automated detection of vulnerable individuals remains limited. Social media platforms like X (formerly Twitter) serve as important venues for people to share their experiences openly. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for the automatic detection of self-reported chronic stress in English tweets. We investigate whether continual pretraining on clinically related conditions, such as depression, anxiety, and PTSD, which have a high comorbidity with chronic stress, improves stress detection compared to general language models. We continually pretrained RoBERTa on the Stress-SMHD corpus, a subset of Self-reported Mental Health Diagnoses focused on stress-related conditions, consisting of 108 million words from users with self-reported diagnoses of depression, anxiety, and PTSD. Then, we fine-tuned on the SMM4H 2022 Shared Task 8. StressRoBERTa achieves 82% F1, which outperforms the best shared task system (79% F1) by 3 percentage points. Our results demonstrate that focused cross-condition transfer learning from stress-related disorders provides stronger representations than general mental health training. To validate cross-condition generalization, we also fine-tuned the model on the Dreaddit. Our result of 81% F1 further demonstrates the transfer from clinical mental health contexts to situational stress discussions.
%U https://aclanthology.org/2026.healing-1.27/
%P 305-313

Markdown (Informal)

[StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection](https://aclanthology.org/2026.healing-1.27/) (Alqahtani et al., HeaLing 2026)

• StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection (Alqahtani et al., HeaLing 2026)

ACL

• Amal Abdullah Alqahtani, Efsun Kayi, and Mona T. Diab. 2026. StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection. In Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026), pages 305–313, Rabat, Morocco. Association for Computational Linguistics.