@inproceedings{stamou-etal-2026-depression,
    title = "Depression Detection in {M}odern {G}reek",
    author = "Stamou, Vivian  and
      Mikros, George  and
      Markopoulos, George  and
      Varlokosta, Spyridoula",
    editor = {Kokkinakis, Dimitrios  and
      Themistocleous, Charalambos  and
      Dias, Ga{\"e}l  and
      Fraser, Kathleen C.  and
      {\"O}hman, Fredrik  and
      Pais, Sebasti{\~a}o},
    booktitle = "Proceedings of the Sixth Resources and {P}rocess{I}ng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments in cooperation with the {MENTAL}.ai consortium",
    month = may,
    year = "2026",
    address = "Palma, Mallorca, Spain",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.rapid-1.9/",
    doi = "10.63317/2gdmrug2fvmw",
    pages = "106--114",
    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."
}