Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego

Abstract

Information diffusion on social media during disasters is an important indicator of community resilience. As a common natural hazard in the U.S., winter storms often cause adverse socio-economic impacts on human society. Understanding people’s perception and behaviours during winter storms is important to mitigate negative impacts and promote community resilience. This study applies text mining and spatial analysis methods on Twitter data during Winter Storm Diego on 2018 December. Different from previous studies focusing on original tweets, this study utilized retweets to model information diffusion in the contiguous United States and analysed the geographic distribution of information flows in various topics. The diffusion extent and direction of the storm-related retweets were compared among different topics. Kernel density maps and standard deviational ellipse were applied to model the spatial distribution of the retweets in different topics. The result shows that people outside of the affected areas expressed more negative sentiment towards the storm than people in the affected areas. Also, distance decay of retweet density has been found and the decay rate differs in different topics. These findings of the analyses will provide support for disaster relief, information communication and broadcasting through social media platforms.