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.
Abstract (translated)
慢性压力的普遍性代表了一个重要的公共健康问题，社交媒体平台如Twitter成为了个人分享其经历的重要场所。本文介绍了StressRoBERTa，这是一种用于自动检测英文推特中自我报告的慢性压力的跨条件迁移学习方法。研究探讨了在与临床相关状况（抑郁症、焦虑症、PTSD）上进行持续训练是否能提高慢性压力检测的效果，而这些状况具有较高的共病性，并且优于一般的语言模型和广泛的心理健康模型。 RoBERTa模型在Stress-SMHD语料库（来自自述患有抑郁症、焦虑症和PTSD的用户，共计1.08亿单词）上进行持续训练，并在SMM4H 2022 Task 8数据集上进行了微调。StressRoBERTA达到了82%的F1值，比共享任务中的最佳系统（79% F1）高出3个百分点。结果显示，从与压力相关疾病的跨条件转移（相对于原始RoBERTa模型提升了1% F1），提供了比一般心理健康训练更强的表现力。 在Dreaddit数据集上的评估进一步表明了从临床心理健康背景到情境性压力讨论的迁移能力，F1值为81%。
URL
https://arxiv.org/abs/2512.23813
