论文标题
在社交媒体上适应心理健康预测的深度学习方法
Adapting Deep Learning Methods for Mental Health Prediction on Social Media
论文作者
论文摘要
心理健康对个人的福祉构成了重大挑战。丰富资源（如社交媒体）的文本分析可以有助于更深入地了解疾病，并为其早期发现提供手段。我们应对通过基于深度学习的模型来检测社交媒体用户的心理状况的挑战，从传统的方法转向任务。在预测用户是否患有九种不同疾病之一的二进制分类任务中，分层注意力网络的表现先前优于四个疾病的基准。此外，我们通过检查模型的单词级别的注意力权重来探索模型的局限性，并分析与分类相关的短语。
Mental health poses a significant challenge for an individual's well-being. Text analysis of rich resources, like social media, can contribute to deeper understanding of illnesses and provide means for their early detection. We tackle a challenge of detecting social media users' mental status through deep learning-based models, moving away from traditional approaches to the task. In a binary classification task on predicting if a user suffers from one of nine different disorders, a hierarchical attention network outperforms previously set benchmarks for four of the disorders. Furthermore, we explore the limitations of our model and analyze phrases relevant for classification by inspecting the model's word-level attention weights.
