@inproceedings{bucur-etal-2025-datasets,
    title = "Datasets for Depression Modeling in Social Media: An Overview",
    author = "Bucur, Ana-Maria  and
      Moldovan, Andreea  and
      Parvatikar, Krutika  and
      Zampieri, Marcos  and
      Khudabukhsh, Ashiqur  and
      Dinu, Liviu",
    editor = "Zirikly, Ayah  and
      Yates, Andrew  and
      Desmet, Bart  and
      Ireland, Molly  and
      Bedrick, Steven  and
      MacAvaney, Sean  and
      Bar, Kfir  and
      Ophir, Yaakov",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.clpsych-1.10/",
    pages = "116--126",
    ISBN = "979-8-89176-226-8",
    abstract = "Depression is the most common mental health disorder, and its prevalence increased during the COVID-19 pandemic. As one of the most extensively researched psychological conditions, recent research has increasingly focused on leveraging social media data to enhance traditional methods of depression screening. This paper addresses the growing interest in interdisciplinary research on depression, and aims to support early-career researchers by providing a comprehensive and up-to-date list of datasets for analyzing and predicting depression through social media data. We present an overview of datasets published between 2019 and 2024. We also make the comprehensive list of datasets available online as a continuously updated resource, with the hope that it will facilitate further interdisciplinary research into the linguistic expressions of depression on social media."
}