Analyzing the evolution of rare events via social media data and k-means clustering algorithm

Recently, many researchers attempt to find relationships between rare events and social media activities. This work proposes a data processing method based on the k-means clustering algorithm and analyze the evolution of a rare event via social media data. We use k-means twice in the spatial and time domains, respectively. The effectiveness of the method is verified by analyzing the damage of a hurricane named Sandy that occurred in 2012. The data set with respect to Sandy is obtained from a very popular social media, Twitter. The results show that our method can precisely predicate the accurate evolution of the hurricane, i.e., the affected place, time and severity. Besides, two new concepts, growth ratio and DRR rate, are presented to analyze the dataset in the time domain.

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