@inproceedings{lee-etal-2022-detecting,
    title = "Detecting Suicidality with a Contextual Graph Neural Network",
    author = "Lee, Daeun  and
      Kang, Migyeong  and
      Kim, Minji  and
      Han, Jinyoung",
    editor = "Zirikly, Ayah  and
      Atzil-Slonim, Dana  and
      Liakata, Maria  and
      Bedrick, Steven  and
      Desmet, Bart  and
      Ireland, Molly  and
      Lee, Andrew  and
      MacAvaney, Sean  and
      Purver, Matthew  and
      Resnik, Rebecca  and
      Yates, Andrew",
    booktitle = "Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology",
    month = jul,
    year = "2022",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2022.clpsych-1.10/",
    doi = "10.18653/v1/2022.clpsych-1.10",
    pages = "116--125",
    abstract = "Discovering individuals' suicidality on social media has become increasingly important. Many researchers have studied to detect suicidality by using a suicide dictionary. However, while prior work focused on matching a word in a post with a suicide dictionary without considering contexts, little attention has been paid to how the word can be associated with the suicide-related context. To address this problem, we propose a suicidality detection model based on a graph neural network to grasp the dynamic semantic information of the suicide vocabulary by learning the relations between a given post and words. The extensive evaluation demonstrates that the proposed model achieves higher performance than the state-of-the-art methods. We believe the proposed model has great utility in identifying the suicidality of individuals and hence preventing individuals from potential suicide risks at an early stage."
}