论文标题
在Twitter中先发制于抑郁和焦虑的焦虑
Towards Preemptive Detection of Depression and Anxiety in Twitter
论文作者
论文摘要
抑郁和焦虑是在日常生活的许多领域都观察到的精神疾病。例如，这些疾病在社交媒体中的非诊断用户撰写的文本中经常表现出来。但是，检测使用这些条件的用户并不是一项简单的任务，因为他们可能不会明确谈论其心理状态，如果这样做，则必须考虑到诸如即时性的情境提示。如果有的话，医学专家可以使用指出可能焦虑或抑郁的语言旗帜来编写更好的准则和治疗方法。在本文中，我们开发了一个旨在在Twitter中促进抑郁和焦虑检测研究的数据集，将检测任务作为二进制推文分类问题进行了框架。然后，我们将最新的分类模型应用于该数据集，并提供一组竞争性基线以及定性错误分析。我们的结果表明，语言模型的表现良好，并且比传统的基线更好。尽管如此，还有明确的改进空间，尤其是在训练集不平衡的情况下，在看似明显的语言提示（关键字）的情况下，违反直觉使用。
Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.
