Automatic depression detection on Twitter can help individuals privately and
conveniently understand their mental health status in the early stages before
seeing mental health professionals. Most existing black-box-like deep learning
methods for depression detection largely focused on improving classification
performance. However, explaining model decisions is imperative in health
research because decision-making can often be high-stakes and life-and-death.
Reliable automatic diagnosis of mental health problems including depression
should be supported by credible explanations justifying models' predictions. In
this work, we propose a novel explainable model for depression detection on
Twitter. It comprises a novel encoder combining hierarchical attention
mechanisms and feed-forward neural networks. To support psycholinguistic
studies, our model leverages metaphorical concept mappings as input. Thus, it
not only detects depressed individuals, but also identifies features of such
users' tweets and associated metaphor concept mappings.

在 Twitter 上进行自动检测抑郁症能够帮助个人在寻求心理健康专业人员帮助之前，以私密和方便的方式了解自己的心理健康状况。本文提出了一种基于描述性模型的新型抑郁症检测模型，该模型结合了分层注意机制和前馈神经网络，在支持心理语言学研究的同时，其还识别了用户推文的特征和相关的隐喻概念映射。