@inproceedings{kshirsagar-etal-2017-detecting,
    title = "Detecting and Explaining Crisis",
    author = "Kshirsagar, Rohan  and
      Morris, Robert  and
      Bowman, Samuel",
    editor = "Hollingshead, Kristy  and
      Ireland, Molly E.  and
      Loveys, Kate",
    booktitle = "Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology {---} From Linguistic Signal to Clinical Reality",
    month = aug,
    year = "2017",
    address = "Vancouver, BC",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-3108/",
    doi = "10.18653/v1/W17-3108",
    pages = "66--73",
    abstract = "Individuals on social media may reveal themselves to be in various states of crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis from social media text automatically and accurately can have profound consequences. However, detecting a general state of crisis without explaining why has limited applications. An explanation in this context is a coherent, concise subset of the text that rationalizes the crisis detection. We explore several methods to detect and explain crisis using a combination of neural and non-neural techniques. We evaluate these techniques on a unique data set obtained from Koko, an anonymous emotional support network available through various messaging applications. We annotate a small subset of the samples labeled with crisis with corresponding explanations. Our best technique significantly outperforms the baseline for detection and explanation."
}