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.

This paper presents a methodological approach for establishing control corpora in the context of depression detection in the Modern Greek language. We discuss various methods used to create control corpora, focusing on the challenge of selecting representative samples from the general population when the target reference is the depressed population. Our approach includes traditional random selection among Twitter users, as well as an innovative method for creating topic-oriented control corpora. Through this study, we provide insights into the development of control corpora, offering valuable considerations for researchers working on similar projects in linguistic analysis and mental health studies. In addition, we identify several dominant topics in the depressed population such as religion, sentiments, health and digestion, which seem to align with findings consistently reported in the literature