StressRoBERTa Detecting Chronic Stress via Social Media Analysis
📝 Original Paper Info
- Title: StressRoBERTa Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection- ArXiv ID: 2512.23813
- Date: 2025-12-29
- Authors: Amal Alqahtani, Efsun Kayi, Mona Diab
📝 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.💡 Summary & Analysis
1. **Contribution 1**: Introduces a new approach to enhancing the performance of deep learning models. - Imagine this like a chef trying different ingredients and methods to create an even tastier dish.-
Contribution 2: Validation of reliability through model performance comparison across various datasets.
- This is akin to testing which type of fruit is the sweetest by tasting many kinds.
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Contribution 3: Demonstrates practical utility through real-world application case studies.
- It’s like a car manufacturer using new technology in test drives to see how well it works in practice.
📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
