Work

Depression Detection in Modern Greek

Year: 2026

Type: conference-paper

Abstract: Despite advancements in NLP-based mental health screening, research remains predominantly English-centric, leaving under-resourced languages insufficiently explored.This study investigates depression detection in Modern Greek social media through a series of experiments.We benchmark traditional machine learning (ML) models against transformer architectures (GreekBERT, GreekSocialBERT, mBERT, and XLM-R) under two settings: a topic-oriented control corpus and a high-similarity stress-test contrasting a gold case of a depressed user with a matched control.Transformer models consistently outperform ML models (F1 = 0.95) but offer limited interpretability.To address this limitation, we incorporate LIWC-derived psycholinguistic features with SHAP explanations to examine model behavior in relation to established linguistic markers.The analysis reveals linguistic patterns consistent with depressive symptoms, such as reduced work-related engagement, social withdrawal, and the motivational deficits characteristically linked to anhedonia in clinical literature.Overall, the results provide a baseline for depression detection in Modern Greek and underscore the importance of grounding automated screening in clinically interpretable evidence.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/2gdmrug2fvmw

Authors: Vivian Stamou, George Mikros, George Markopoulos, Spyridoula Varlokosta

Institutions: National and Kapodistrian University of Athens, Hamad bin Khalifa University

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7166571365