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.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure