Dynamic Spatio-Temporal Tweet Mining for Event Detection: A Case Study of Hurricane Florence

Mahdi Farnaghi; Zeinab Ghaemi; Ali Mansourian

doi:10.1007/s13753-020-00280-z

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Article Navigation > International Journal of Disaster Risk Science > 2020 > 11(3): 378-393

Mahdi Farnaghi, Zeinab Ghaemi, Ali Mansourian. Dynamic Spatio-Temporal Tweet Mining for Event Detection: A Case Study of Hurricane Florence[J]. International Journal of Disaster Risk Science, 2020, 11(3): 378-393. doi: 10.1007/s13753-020-00280-z

Citation:

Mahdi Farnaghi, Zeinab Ghaemi, Ali Mansourian. Dynamic Spatio-Temporal Tweet Mining for Event Detection: A Case Study of Hurricane Florence[J]. International Journal of Disaster Risk Science, 2020, 11(3): 378-393. doi: 10.1007/s13753-020-00280-z

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Dynamic Spatio-Temporal Tweet Mining for Event Detection: A Case Study of Hurricane Florence

doi: 10.1007/s13753-020-00280-z

1 GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden;
2 Faculty of Geodesy and Geomatics, K.N. Toosi University of Technology, Tehran, Iran;
3 Center for Middle Eastern Studies, Lund University, 221 00 Lund, Sweden

Available Online: 2021-04-26

Abstract

Abstract

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 disaster. This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas. It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data. To precisely calculate the textual similarity, three state-of-the-art text embedding methods of Word2vec, GloVe, and FastText were used to capture both syntactic and semantic similarities. The impact of selected embedding algorithms on the quality of the outputs was studied. Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes. The proposed method was applied to a case study related to 2018 Hurricane Florence. The method was able to precisely identify events of varied sizes and densities before, during, and after the hurricane. The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed. The 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.
- Disaster management,
- Hurricane Florence,
- Natural language processing,
- Spatio-temporal tweet analysis,
- Tweet clustering,
- Twitter

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