Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular $ERDE$ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.

中文翻译：

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利用神经网络和语言元数据对文本序列中的抑郁迹象进行早期检测

抑郁症被列为全球残疾的最大因素，也是自杀的主要原因。尽管如此，许多患有抑郁症的人由于各种原因没有得到治疗。先前的研究表明，抑郁症也会影响语言使用，许多抑郁症患者通常使用社交媒体平台或互联网来获取信息或讨论他们的问题。本文讨论了使用基于社交平台上的消息的机器学习模型对抑郁症的早期检测。特别是，评估了基于不同词嵌入的卷积神经网络，并将其与基于用户级语言元数据的分类进行了比较。两种方法的集成显示出在当前的早期检测任务中实现最先进的结果。此外，$ERDE$乙电阻D乙详细检查了作为早期检测系统度量的分数，并说明了它在共享任务的上下文中的缺点。提出了一个稍微修改的度量标准，并与原始分数进行比较。最后，在与所描述任务相同领域的大型语料库上训练新词嵌入并进行评估。