Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting users' symptom severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\% improvement over state of the art in terms of measuring depression severity.

翻译：抑郁症是全球范围内严重的公共卫生问题。然而，公共卫生系统在病例识别与诊断方面的能力有限。在此背景下，社交媒体的广泛使用为大规模获取公共信息开辟了途径。计算模型可通过利用用户生成的社交媒体内容，作为快速筛查的辅助工具。本文提出了一种高效的语义处理流程，基于个体在社交媒体中的写作内容研究其抑郁症严重程度。我们选取测试用户的语句，针对一组代表抑郁症状及严重程度的训练句子生成语义排名，随后将排名结果中的句子作为证据，用于预测用户的症状严重程度。为此，我们探索了不同聚合方法，以对应每个症状的贝克抑郁量表（BDI）四项选项之一。我们在两个基于Reddit的基准数据集上评估了所提方法，在抑郁症严重程度评估方面较现有最优结果提升了30%。