Evaluating the Approach to Detecting and Monitoring Social Media Events to Combat Natural Disasters

Abstract

Three main stages in the approach to identifying news on natural disasters and the clustering groups of citizens are considered. The first step presents the sequence of performing several natural language processing tasks. The problem of the ambiguity and vagueness of news similarities has been ignored in traditional methods of event detection. To this end, the second step is to apply fuzzy set techniques to the events extracted to improve the quality of clustering and to eliminate the vagueness of the extracted information. A certain degree of hazard is then entered as input to the citizen clustering method to identify communities that feature similar degrees of distress. The results show that with the help of the proposed approach, it is possible to identify homogeneous and compact clusters of citizens.