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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 2, 2021
https://doi.org/10.5194/agile-giss-2-2-2021
AGILE GIScience Ser., 2, 2, 2021
https://doi.org/10.5194/agile-giss-2-2-2021

  04 Jun 2021

04 Jun 2021

H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?

Rémy Decoupes1, Rodrique Kafando1, Mathieu Roche1,2, and Maguelonne Teisseire1 Rémy Decoupes et al.
  • 1TETIS, Univ Montpellier, AgroParisTech, CIRAD, CNRS, INRAE, Montpellier, France
  • 2CIRAD, F-34398 Montpellier, France

Keywords: TF-IDF, Hierarchical analysis, Pandemic situation, social network

Abstract. Data produced by social networks may contain weak signals of possible epidemic outbreaks. In this paper, we focus on Twitter data during the waiting period before the appearance of COVID-19 first cases outside China. Among the huge flow of tweets that reflects a global growing concern in all countries, we propose to analyze such data with an adaptation of the TF-IDF measure. It allows the users to extract the discriminant vocabularies used across time and space. The results are then discussed to show how the specific spatio-temporal anchoring of the extracted terms make it possible to follow the crisis dynamics on different scales of time and space.

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