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StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection

Amal Alqahtani, Efsun Kayi, Mona Diab

TL;DR

StressRoBERTa tackles automatic self-reported chronic stress detection on Twitter by harnessing cross-condition transfer from depression, anxiety, and PTSD. The model undergoes domain-adaptive continual training on Stress-SMHD before fine-tuning on SMM4H 2022 Task 8, achieving 82% F1 and outperforming the best shared-task system. Cross-platform evaluation on Dreaddit confirms transfer from clinical mental-health contexts to general stress discourse, with 81% F1. The results indicate that a focused, clinically related continual-training strategy yields stronger stress representations than general mental-health training, with practical implications for scalable stress monitoring under ethical safeguards.

Abstract

The prevalence of chronic stress represents a significant public health concern, with social media platforms like Twitter serving as important venues for individuals to share their experiences. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for automatic detection of self-reported chronic stress in English tweets. The investigation examines whether continual training on clinically related conditions (depression, anxiety, PTSD), disorders with high comorbidity with chronic stress, improves stress detection compared to general language models and broad mental health models. RoBERTa is continually trained on the Stress-SMHD corpus (108M words from users with self-reported diagnoses of depression, anxiety, and PTSD) and fine-tuned on the SMM4H 2022 Task 8 dataset. StressRoBERTa achieves 82% F1-score, outperforming the best shared task system (79% F1) by 3 percentage points. The results demonstrate that focused cross-condition transfer from stress-related disorders (+1% F1 over vanilla RoBERTa) provides stronger representations than general mental health training. Evaluation on Dreaddit (81% F1) further demonstrates transfer from clinical mental health contexts to situational stress discussions.

StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection

TL;DR

StressRoBERTa tackles automatic self-reported chronic stress detection on Twitter by harnessing cross-condition transfer from depression, anxiety, and PTSD. The model undergoes domain-adaptive continual training on Stress-SMHD before fine-tuning on SMM4H 2022 Task 8, achieving 82% F1 and outperforming the best shared-task system. Cross-platform evaluation on Dreaddit confirms transfer from clinical mental-health contexts to general stress discourse, with 81% F1. The results indicate that a focused, clinically related continual-training strategy yields stronger stress representations than general mental-health training, with practical implications for scalable stress monitoring under ethical safeguards.

Abstract

The prevalence of chronic stress represents a significant public health concern, with social media platforms like Twitter serving as important venues for individuals to share their experiences. This paper introduces StressRoBERTa, a cross-condition transfer learning approach for automatic detection of self-reported chronic stress in English tweets. The investigation examines whether continual training on clinically related conditions (depression, anxiety, PTSD), disorders with high comorbidity with chronic stress, improves stress detection compared to general language models and broad mental health models. RoBERTa is continually trained on the Stress-SMHD corpus (108M words from users with self-reported diagnoses of depression, anxiety, and PTSD) and fine-tuned on the SMM4H 2022 Task 8 dataset. StressRoBERTa achieves 82% F1-score, outperforming the best shared task system (79% F1) by 3 percentage points. The results demonstrate that focused cross-condition transfer from stress-related disorders (+1% F1 over vanilla RoBERTa) provides stronger representations than general mental health training. Evaluation on Dreaddit (81% F1) further demonstrates transfer from clinical mental health contexts to situational stress discussions.
Paper Structure (30 sections, 2 figures, 8 tables)

This paper contains 30 sections, 2 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Continually Trained Large Language Models for Mental Health
  4. SMM4H Shared Task 8: Stress Detection on Twitter
  5. Method and Setup
  6. Language Model Continual Training
  7. Continual Training Corpus
  8. Stress-SMHD
  9. StressRoBERTa Model
  10. Continual Training Configuration
  11. Evaluation
  12. Datasets
  13. SMM4H 2022 Shared Task 8: Twitter Stress Detection
  14. Dreaddit: Reddit Stress Detection
  15. Experimental Setup
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Overview of StressRoBERTa cross-condition transfer learning methodology
  • Figure 2: Detailed overview of StressRoBERTa cross-condition transfer learning pipeline