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
- Venue:
- CLPsych
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 66–73
- Language:
- URL:
- https://aclanthology.org/W17-3108
- DOI:
- 10.18653/v1/W17-3108
- 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., CLPsych 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-3108.pdf