论文标题
在Twitter上可解释的抑郁症检测的分层注意力网络在隐喻概念映射的帮助下
Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
论文作者
论文摘要
在Twitter上的自动抑郁症检测可以帮助个人在早期阶段私下，方便地了解其心理健康状况，然后再见到心理健康专业人员。大多数现有的黑盒状深学习方法用于抑郁症检测，主要集中于改善分类性能。但是，在健康研究中，解释模型决策至关重要，因为决策通常可以是高风险和生命和死亡。可靠的自动诊断心理健康问题在内的抑郁症应有可靠的解释来证明合理的模型预测。在这项工作中，我们提出了一个新颖的可解释模型，用于在Twitter上检测抑郁症。它包括一个新颖的编码器，结合了分层注意机制和前馈神经网络。为了支持心理语言学研究，我们的模型利用隐喻概念映射作为输入。因此，它不仅检测到沮丧的人，还可以确定此类用户推文和相关隐喻概念映射的功能。
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.
