Using centrality measures to improve the classification performance of tweets during natural disasters

Abstract

Online social networks like Twitter facilitate instant communication during natural disasters. A key problem is to distinguish in real-time the most assertive and contingent tweets related to the current disaster from the whole streaming. To address this problem, centrality measures are proposed to improve the training data sample of active learning classifiers. As a case study, tweets collected during the massive floods in Santiago of Chile in 2016 are considered. This approach improves the consistency and pertinence of the labeling process, as well as the classifiers' performance.