The World Health Organization (WHO) predicts that depression disorders will be widespread in the next 20 years. These disorders may affect a person’s general health and habits such as altered sleeping and eating patterns in addition to their interpersonal relationships. Early depression detection and prevention therefore becomes an important issue. To address this critical issue, we recruited 1453 individuals who use Facebook frequently and collected their Facebook data. We then propose an automatic depression detection approach, named Deep Learning-based Depression Detection with Heterogeneous Data Sources (D3-HDS), to predict the depression label of an individual by analyzing his/her living environment, behavior, and the posting contents in the social media. The proposed method employs Recurrent Neural Networks to compute the posts representation of each individual. The representations are then combined with other content-based, behavior and living environment features to predict the depression label of the individual with Deep Neural Networks. To our best knowledge, this is the first attempt that simultaneously considers all the content-based, behavior, and living environment features for depression detection. The experiment results on a real dataset show that the performance of our approach significantly outperforms the other baselines.

中文翻译：

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使用发布、行为和生活环境数据进行抑郁症检测的深层架构

世界卫生组织 (WHO) 预测，抑郁症将在未来 20 年内普遍存在。这些障碍可能会影响一个人的整体健康和习惯，例如改变睡眠和饮食模式以及他们的人际关系。因此，早期抑郁症的检测和预防成为一个重要问题。为了解决这个关键问题，我们招募了 1453 名经常使用 Facebook 的人并收集了他们的 Facebook 数据。然后，我们提出了一种自动抑郁检测方法，称为基于异构数据源的深度学习抑郁检测（D3-HDS），通过分析个人的生活环境、行为和发布内容来预测他/她的抑郁标签。社交媒体。所提出的方法采用循环神经网络来计算每个人的帖子表示。然后将这些表征与其他基于内容、行为和生活环境的特征结合起来，用深度神经网络预测个体的抑郁标签。据我们所知，这是第一次同时考虑所有基于内容、行为和生活环境特征的抑郁症检测尝试。在真实数据集上的实验结果表明，我们的方法的性能明显优于其他基线。这是第一次同时考虑所有基于内容、行为和生活环境特征的抑郁症检测尝试。在真实数据集上的实验结果表明，我们的方法的性能明显优于其他基线。这是第一次同时考虑所有基于内容、行为和生活环境特征的抑郁症检测尝试。在真实数据集上的实验结果表明，我们的方法的性能明显优于其他基线。