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

M. Farnaghi*, Zeinab Ghaemi, Ali Mansourian

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)378-393
Number of pages16
JournalInternational Journal of Disaster Risk Science
Volume11
Early online date26 May 2020
DOIs
Publication statusPublished - 1 Jun 2020
Externally publishedYes

Keywords

  • Disaster management
  • Hurricane Florence
  • Natural language processing
  • Spatio-temporal tweet analysis
  • Tweet clustering
  • Twitter
  • ITC-CV

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