Emotions-Based Disaster Tweets Classification: Real or Fake

Abstract

Social media platforms are considered interactive communication channels between governments, civil society organizations, and the public. During disaster occurrences, social media platforms play a crucial role such as the alertness of people towards the disaster occurrence, its risks, and consequences. They are used as tools to spread real updated information rapidly related to the disaster. Furthermore, social media platforms can facilitate the mobilization of volunteers as well as the organization of campaign donations after the disaster occurrence. Nevertheless, the benefits of social media platforms can be a double-edged sword through the dissemination of unreal information such as rumors or fake disasters. Unfortunately, the public can easily believe unreal information due to the anxiety that they experienced during the occurrence of a past real disaster. This paper presents a model to distinguish between the fake disaster tweets and the real ones. The implementation of this model is established twice; the first implementation involves the use of Machine Learning with the traditional Natural Language Processing techniques on the disaster dataset provided by Kaggle, and the second implementation involves using the emotions that are extracted from the tweets in the classification process. The proposed model achieves an accuracy of 88,34% without the usage of the emotion extraction module while it achieves an accuracy of 89,39 % with the inclusion of the emotion extraction module.