aakash-agarwal/ReDepress

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Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at ad2d543
Licence files: LICENSE, licence not identifiedEvidence: LICENSE at ad2d543
Declared sources: None listed in the card metadata
Access gated on Hugging Face: Yes, manual approval required

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Citation text from the card

@inproceedings{agarwal-etal-2025-redepress,
    title = "{R}e{D}epress: A Cognitive Framework for Detecting Depression Relapse from Social Media",
    author = "Agarwal, Aakash Kumar  and
      Bhattacharjee, Saprativa  and
      Rastogi, Mauli  and
      Jacob, Jemima S.  and
      Banerjee, Biplab  and
      Gupta, Rashmi  and
      Bhattacharyya, Pushpak",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.emnlp-main.1758/",
    pages = "34652--34670",
    ISBN = "979-8-89176-332-6",
    abstract = "Almost 50{\%} depression patients face the risk of going into relapse. The risk increases to 80{\%} after the second episode of depression. Although, depression detection from social media has attained considerable attention, depression relapse detection has remained largely unexplored due to the lack of curated datasets and the difficulty of distinguishing relapse and non-relapse users. In this work, we present ReDepress, the first clinically validated social media dataset focused on relapse, comprising 204 Reddit users annotated by mental health professionals. Unlike prior approaches, our framework draws on cognitive theories of depr

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dataset_licence({ "id": "aakash-agarwal/ReDepress" })
signal: "unclear"
stated_licence: null
sources: 0