论文标题
使用学习的信心分数从社交媒体文本中检测到抑郁症的早期发作
Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores
论文作者
论文摘要
关于书面文本的心理健康障碍的计算研究涵盖了自然语言处理与心理学之间的跨学科领域。这个问题的关键方面是预防和早期诊断，因为自杀是由于抑郁症是年轻人的第二大死亡原因。在这项工作中，我们专注于检测社交媒体文本（尤其是Reddit）早期抑郁症发作的方法。为此，我们通过利用主题分析并学习信心分数来指导决策过程，探索ERISK 2018数据集并在最新情况下取得良好的结果。
Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.
