@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting and Explaining Crisis
%A Kshirsagar, Rohan
%A Morris, Robert
%A Bowman, Samuel
%Y Hollingshead, Kristy
%Y Ireland, Molly E.
%Y Loveys, Kate
%S Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC
%F kshirsagar-etal-2017-detecting
%X 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.
%R 10.18653/v1/W17-3108
%U https://aclanthology.org/W17-3108/
%U https://doi.org/10.18653/v1/W17-3108
%P 66-73
Markdown (Informal)
[Detecting and Explaining Crisis](https://aclanthology.org/W17-3108/) (Kshirsagar et al., CLPsych 2017)
ACL
- Rohan Kshirsagar, Robert Morris, and Samuel Bowman. 2017. Detecting and Explaining Crisis. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 66–73, Vancouver, BC. Association for Computational Linguistics.