@inproceedings{zanwar-etal-2023-smhd,
    title = "{SMHD}-{GER}: A Large-Scale Benchmark Dataset for Automatic Mental Health Detection from Social Media in {G}erman",
    author = "Zanwar, Sourabh  and
      Wiechmann, Daniel  and
      Qiao, Yu  and
      Kerz, Elma",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2023.findings-eacl.113/",
    doi = "10.18653/v1/2023.findings-eacl.113",
    pages = "1526--1541",
    abstract = "Mental health problems are a challenge to our modern society, and their prevalence is predicted to increase worldwide. Recently, a surge of research has demonstrated the potential of automated detection of mental health conditions (MHC) through social media posts, with the ultimate goal of enabling early intervention and monitoring population-level health outcomes in real-time. Progress in this area of research is highly dependent on the availability of high-quality datasets and benchmark corpora. However, the publicly available datasets for understanding and modelling MHC are largely confined to the English language. In this paper, we introduce SMHD-GER (Self-Reported Mental Health Diagnoses for German), a large-scale, carefully constructed dataset for MHC detection built on high-precision patterns and the approach proposed for English. We provide benchmark models for this dataset to facilitate further research and conduct extensive experiments. These models leverage engineered (psycho-)linguistic features as well as BERT-German. We also examine nuanced patterns of linguistic markers characteristics of specific MHC."
}