# Detecting and Explaining Crisis

**Authors:** Rohan Kshirsagar, Robert Morris, Sam Bowman  
**arXiv:** [1705.09585](https://arxiv.org/abs/1705.09585)  
**Date:** 2017-05-26  
**Summary page:** https://www.summarizepaper.com/fr/paper/1705.09585v1/

## 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.