We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clinically relevant features, e.g., depression scores and their consequent temporal patterns, as well as user posting activity patterns, e.g., quantifying their ``no activity'' or ``silence.'' Furthermore, to evaluate the efficacy of these extracted features, we create three kinds of datasets including a test dataset, from two existing well-known benchmark datasets for user-level depression detection. We then provide accuracy measures based on single features, baseline features and feature ablation tests, at several different levels of temporal granularity. The relevant data distributions and clinical depression detection related settings can be exploited to draw a complete picture of the impact of different features across our created datasets. Finally, we show that, in general, only semantic oriented representation models perform well. However, clinical features may enhance overall performance provided that the training and testing distribution is similar, and there is more data in a user's timeline. The consequence is that the predictive capability of depression scores increase significantly while used in a more sensitive clinical depression detection settings.

翻译：我们描述了一种基于用户时序社交媒体帖子检测用户层面临床抑郁症的模型开发过程。该模型采用抑郁症状检测（DSD）分类器，该分类器基于现有规模最大的临床医生标注的抑郁症状推文样本进行训练。我们随后利用DSD模型提取临床相关特征，例如抑郁评分及其相应的时序模式，以及用户发帖活动模式（如量化其“无活动”或“沉默期”）。为评估这些提取特征的有效性，我们从两个现有知名的用户层面抑郁症检测基准数据集中构建了三类数据集（含测试集）。基于单一特征、基线特征及特征消融实验，我们在多个时间粒度层面提供了准确性度量指标。通过利用相关数据分布与临床抑郁症检测相关设置，可全面描绘不同特征在我们所创建数据集中的影响力图谱。最终研究表明：一般而言，仅语义导向表征模型表现良好；但若训练集与测试集分布相似且用户时间轴数据量充足，临床特征可提升整体性能。其结果是，在更敏感的临床抑郁症检测场景中应用时，抑郁评分的预测能力显著提升。