Mining typhoon victim information based on multi-source data fusion using social media data in China: a case study of the 2019 Super Typhoon Lekima

Abstract

Based on situational awareness and information sharing, social media are regarded as a significant data source for disaster emergency management. Many studies have shown that social media can be used for rapid damage assessments during typhoon disasters, while few studies were able to extract victim information through social media. This study aims to determine whether and how we can mine accurate typhoon victim locations and spatial distributions using microblogs from Sina Weibo, one of the largest social media platforms in China, using a case study of the 2019 Super Typhoon Lekima. We first used the latent Dirichlet allocation (LDA) algorithm to classify disaster-related microblogs and exclude irrelevant information. Then, the SnowNLP library was applied to calculate the sentiment score. The negative sentiment contained victim and injury information but was not specific enough. Finally, Euclidean distance and Euclidean distance considering vulnerability were select to identify victim locations 72 hours after Lekima landed using Ordering Point to Identify the Cluster Structure (OPTICS) algorithm. Compared to the real victim locations, the hit rate of the former was 23.5%, while the latter was 31.8%. These results demonstrate that victim information recognition based on multi-source data fusion from Sina Weibo could be an effective new method for assisting disaster emergency response and rescue during typhoons.