Social Media Disaster Relevance Classification for Situation Awareness during Emergency Response in Indonesia

Abstract

Natural disasters are expected to increase in number and severity on a global scale. Social media analysis has become an essential tool in natural disaster management on tracking disaster events, impact, and other relevant critical information. However, the high volume of tweets data produces noise, and not all tweets are relevant to gain situational awareness for disaster response. This paper presents a disaster-relevance classification for Indonesian language tweets using machine learning with Naïve Bayes, support vector machine, and logistic regression with a focus on twitter data generated during the Sulawesi Earthquake, Indonesia 2018. With the result accuracy of 83.5%, our labeled data can be used for capturing disaster-relevant tweets in any future disaster event in Indonesia.