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

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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https://hal.inrae.fr/hal-03279146
Contributor : Rodrique Kafando <>
Submitted on : Tuesday, July 6, 2021 - 12:00:21 PM
Last modification on : Tuesday, September 7, 2021 - 3:44:28 PM

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Rémy Decoupes, Rodrique Kafando, Mathieu Roche, Maguelonne Teisseire. H-TFIDF: What makes areas specific over time in the massive flow of tweets related to the covid pandemic?. AGILE: GIScience Series, 2021, 2 (2), pp.1-8. ⟨10.5194/agile-giss-2-2-2021⟩. ⟨hal-03279146⟩

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