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.

本文旨在通过神经技术与其他方法结合，针对社交媒体的文本实现危机检测与解释。通过 Koko 等应用程序中的情感支持网络等独特数据集，获得的样本数据被标注以符合其危机性质及解释，探索了多种方法后我们的最佳技术方案在检测和解释方面表现显著超越了基线。