Detection of Behavioral Facilitation information in Disaster Situation

Abstract

Disasters of many types have occurred in recent years, such as strong earthquakes, heavy rain, and typhoons. In such disaster situations, people often use social network services (SNS) and exchange information of all types to help each other. Especially, people exchange information using Twitter during disasters. Such tweet messages include much information that promotes people's behaviors. We designate such tweets as behavioral facilitation tweets. When psychologically unstable in the aftermath of a disaster, behavioral facilitation tweets can strongly affect people, irrespective of a message's authenticity. We regard the extraction of the behavioral facilitation tweets automatically as important. In this paper, we propose a method that extracts behavioral facilitation tweets in disaster situations. Specifically, we propose and compare three methods to extract behavioral facilitation tweets in disaster situations: rule-based, support vector machine (SVM) and long short-term memory (LSTM). Furthermore, we conducted experiments to assess the benefits of our proposed method.