Abstract

Approximately one-third of the veteran population suffers from post-traumatic stress disorder, a mental illness that is often co-morbid with social anxiety disorder. Student veterans are especially vulnerable as they struggle to adapt to a new, less structured lifestyle with few peers who understand their difficulties. To support mental health experts in the treatment of social anxiety disorder, this study utilized machine learning to detect anxiety in text transcribed from interviews with patients and applied topic modeling to highlight common stress factors for student veterans. We approach our anxiety detection task by exploring both deep learning and traditional machine learning strategies such as transformers, transfer learning, and support vector classifiers. Our models provide a tool to support psychologists and social workers in treating social anxiety. The results detailed in this paper could also have broader impacts in fields such as pedagogy and public health.¹1 The code of the experiments of this study can be found in: https://github.com/imics-lab/text-analysis

摘要

大约三分之一的退伍军人患有创伤后应激障碍，这是一种通常与社交焦虑症并存的精神疾病。退伍军人学生尤其容易受到伤害，因为他们很难适应新的、结构松散的生活方式，而且很少有同龄人理解他们的困难。为了支持心理健康专家治疗社交焦虑症，这项研究利用机器学习来检测从患者访谈中转录的文本中的焦虑，并应用主题模型来突出学生退伍军人的常见压力因素。我们通过探索深度学习和传统机器学习策略（例如变压器、迁移学习和支持向量分类器）来完成焦虑检测任务。我们的模型提供了一种工具来支持心理学家和社会工作者治疗社交焦虑。本文详细介绍的结果还可能对教育学和公共卫生等领域产生更广泛的影响。¹1 本研究的实验代码可参见：https://github.com/imics-lab/text-analysis