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Dynamic spatio-temporal tweet Mining for Event Detection:A Case Study of Hurricane Florence

International Journal of Disaster Risk Science 2020年第3期11卷 378-393页

作者： Mahdi Farnaghi Zeinab Ghaemi Ali Mansourian GIS Center Department of Physical Geography and Ecosystem ScienceLund University22362 LundSweden Faculty of Geodesy and Geomatics K.N.Toosi University of TechnologyTehranIran Center for Middle Eastern Studies Lund University22100 LundSweden

Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a *** study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study *** introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter *** precisely calculate the textual similarity,three state-of-theart text embedding methods of Word2vec,GloVe,and Fast Text were used to capture both syntactic and semantic *** impact of selected embedding algorithms on the quality of the outputs was *** combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection *** proposed method was applied to a case study related to 2018 Hurricane *** method was able to precisely identify events of varied sizes and densities before,during,and after the *** feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively *** proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm,where it showed more promising results.

关键词： Disaster management Hurricane Florence Natural language processing spatio-temporal tweet analysis tweet clustering Twitter

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