Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake

We developed a Multinomial Nave Bayes Classifier to categorize the microblog posts into five types according to the text content of posts.Different types of information had significantly different propagation patterns in terms of scale and topological features.Social media users exhibited significantly different interaction patterns for different types of information at different stages. Social media has been playing an increasingly important role in information publishing and event monitoring in emergencies like natural disasters. The propagation of different types of information on social media is critical in understanding the reaction and mobility of social media users during natural disasters. In this research, we analyzed the dynamic social networks formed by the reposting (retweeting) behaviors in Weibo.com (the major microblog service in China) during Yiliang Earthquake. We developed a Multinomial Nave Bayes Classifier to categorize the microblog posts into five types based on the content, and then characterized the information propagation patterns of the five types of information at different stages after the earthquake occurred. We found that the type of information has significant influence on the propagation patterns in terms of scale and topological features. This research revealed the important role of information type in the publicity and propagation of disaster-related information, thus generated data-driven insights for timely and efficient emergency management using the publicly available social media data.

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