Detecting and Explaining Crisis

Rohan Kshirsagar, Robert Morris, Samuel Bowman


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.
Anthology ID:
W17-3108
Volume:
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
Month:
August
Year:
2017
Address:
Vancouver, BC
Venues:
CLPsych | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
66–73
Language:
URL:
https://aclanthology.org/W17-3108
DOI:
10.18653/v1/W17-3108
Bibkey:
Cite (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.
Cite (Informal):
Detecting and Explaining Crisis (Kshirsagar et al., 2017)
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PDF:
https://preview.aclanthology.org/update-css-js/W17-3108.pdf