用抑郁焦虑等病历数据训练模型，提升社交媒体压力检测准确率。
StressRoBERTa: Cross-Condition Transfer Learning from Depression, Anxiety, and PTSD to Stress Detection
- 从抑郁症等临床数据持续训练，迁移至压力检测任务
- 在SMM4H数据集上达82% F1，比最佳系统高3个百分点
- 适合关注心理健康监测与跨病症模型迁移的研究者
慢性压力的普遍性构成重大公共健康问题，社交媒体如推特成为用户分享经历的重要平台。本文提出StressRoBERTa，一种面向英文推文中文本自我报告慢性压力的跨病症迁移学习方法。研究探讨在具有高共病性的抑郁、焦虑、创伤后应激障碍（PTSD）等临床相关病症上持续训练，是否优于通用语言模型和广泛心理卫生模型。基于包含10800万词的Stress-SMHD语料库（来自自报患有抑郁、焦虑和PTSD的用户）对RoBERTa进行持续训练，并在SMM4H 2022任务8数据集上微调。StressRoBERTa取得82% F1分数，优于最佳共享任务系统（79% F1）3个百分点。结果表明，聚焦于压力相关疾病的知识迁移（较原始RoBERTa提升1% F1）提供了更强表征能力，优于通用心理卫生训练。在Dreaddit数据集上进一步实现81% F1，验证了从临床心理语境向情境性压力讨论的迁移有效性。
原文摘要 · Abstract (English)
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.
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