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Ali Mansourian

Professor

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

Författare

  • Mahdi Farnaghi
  • Zeinab Ghaemi
  • Ali Mansourian

Summary, in English

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.

Avdelning/ar

  • Centrum för geografiska informationssystem (GIS-centrum)
  • MECW: The Middle East in the Contemporary World
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • Centrum för Mellanösternstudier (CMES)

Publiceringsår

2020-06

Språk

Engelska

Sidor

378-393

Publikation/Tidskrift/Serie

International Journal of Disaster Risk Science

Volym

11

Issue

3

Dokumenttyp

Artikel i tidskrift

Förlag

Springer

Ämne

  • Environmental Sciences
  • Geosciences, Multidisciplinary

Nyckelord

  • Disaster management
  • Hurricane Florence
  • Natural language processing
  • Spatio-temporal tweet analysis
  • Tweet clustering
  • Twitter
  • Machine Learning (ML)

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2095-0055