@inproceedings{wang-etal-2025-end,
    title = "End-to-End Learnable Psychiatric Scale Guided Risky Post Screening for Depression Detection on Social Media",
    author = "Wang, Bichen  and
      Zi, Yuzhe  and
      Sun, Yixin  and
      Yang, Hao  and
      Zhao, Yanyan  and
      Qin, Bing",
    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://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.201/",
    pages = "4054--4066",
    ISBN = "979-8-89176-332-6",
    abstract = "Detecting depression through users' social media posting history is crucial for enabling timely intervention; however, irrelevant content within these posts negatively impacts detection performance. Thus, it is crucial to extract pertinent content from users' complex posting history. Current methods utilize frozen screening models, which can miss critical information and limit overall performance due to isolated screening and detection processes. To address these limitations, we propose **E2-LPS** **E**nd-to-**E**nd **L**earnable **P**sychiatric Scale Guided Risky Post **S**creening Model) for jointly training our screening model, guided by psychiatric scales, alongside the detection model. We employ a straight-through estimator to enable a learnable end-to-end screening process and avoid the non-differentiability of the screening process. Experimental results show that E2-LPS outperforms several strong baseline methods, and qualitative analysis confirms that it better captures users' mental states than others."
}