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Comparing Learning-Based Methods for Identifying Disaster-Related Tweets

During natural disaster events, social media users generate a significant amount of data, some of which are valuable for relief efforts and emergency management. In this paper, we study the nature of social media content generated during two natural disasters. We provide a system for labeling Twitter data and classifying disaster-related tweets. The focus is on Twitter data generated before, during, and after Hurricane Florence and Hurricane Michael, both of which occurred in 2018. Then we apply various machine learning models on the labeled data and provide a performance comparison between the models using features generated from TfidfVectorizer and CountVectorizer Approaches.