@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Depression Detection in Modern Greek
%A Stamou, Vivian
%A Mikros, George
%A Markopoulos, George
%A Varlokosta, Spyridoula
%Y Kokkinakis, Dimitrios
%Y Themistocleous, Charalambos
%Y Dias, Gaël
%Y Fraser, Kathleen C.
%Y Öhman, Fredrik
%Y Pais, Sebastião
%S Proceedings of the Sixth Resources and ProcessIng 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
%D 2026
%8 May
%I European Language Resources Association (ELRA)
%C Palma, Mallorca, Spain
%F stamou-etal-2026-depression
%X 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.
%R 10.63317/2gdmrug2fvmw
%U https://aclanthology.org/2026.rapid-1.9/
%U https://doi.org/10.63317/2gdmrug2fvmw
%P 106-114
Markdown (Informal)
[Depression Detection in Modern Greek](https://aclanthology.org/2026.rapid-1.9/) (Stamou et al., RaPID 2026)
ACL
- Vivian Stamou, George Mikros, George Markopoulos, and Spyridoula Varlokosta. 2026. Depression Detection in Modern Greek. In Proceedings of the Sixth Resources and ProcessIng 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, pages 106–114, Palma, Mallorca, Spain. European Language Resources Association (ELRA).