ArXiv Preprint
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.
Anxo Pérez, Neha Warikoo, Kexin Wang, Javier Parapar, Iryna Gurevych
2022-11-14
