Identifying Fine-grained Depression Signs in Social Media Posts

Augusto R. Mendes, Helena Caseli

Abstract
Natural Language Processing has already proven to be an effective tool for helping in the identification of mental health disorders in text. However, most studies limit themselves to a binary classification setup or base their label set on pre-established resources. By doing so, they don’t explicitly model many common ways users can express their depression online, limiting our understanding of what kind of depression signs such models can accurately classify. This study evaluates how machine learning techniques deal with the classification of a fine-grained set of 21 depression signs in social media posts from Brazilian undergraduate students. We found out that model performance is not necessarily driven by a depression sign’s frequency on social media posts, since evaluated machine learning techniques struggle to classify the majority of signs of depression typically present in posts. Thus, model performance seems to be more related to the inherent difficulty of identifying a given sign than with its occurrence frequency.
Anthology ID:
2024.lrec-main.754
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
8594–8604
Language:
URL:
https://aclanthology.org/2024.lrec-main.754
DOI:
Bibkey:
Cite (ACL):
Augusto R. Mendes and Helena Caseli. 2024. Identifying Fine-grained Depression Signs in Social Media Posts. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8594–8604, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Identifying Fine-grained Depression Signs in Social Media Posts (Mendes & Caseli, LREC-COLING 2024)
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