Detecting and Explaining Crisis

TL;DR

This paper presents methods for automatically detecting and explaining crises in social media text, using neural and non-neural techniques evaluated on data from an emotional support network.

Contribution

It introduces a combined approach for crisis detection and explanation, with a new dataset and annotations, outperforming baseline methods.

Findings

Best technique significantly outperforms baseline in detection

Effective explanation methods identify relevant text segments

Dataset from Koko supports realistic crisis detection scenarios

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.