Semantic Similarity Models for Depression Severity Estimation
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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.
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