Semantic Similarity Models for Depression Severity Estimation

TL;DR

This paper introduces a semantic pipeline leveraging social media data to estimate depression severity, achieving significant improvements over existing methods in accuracy and scalability.

Contribution

The paper presents a novel semantic ranking approach combined with aggregation techniques for depression severity estimation from social media texts.

Findings

30% improvement over state-of-the-art in depression severity measurement

Effective semantic ranking method for social media content

Validated on two Reddit-based benchmarks

Abstract

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.