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Topic Modeling for Tracking COVID-19 Communication on Twitter

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Information and Software Technologies (ICIST 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1665))

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Abstract

In this study, we analyze the trends of COVID-19 related communication in Croatian language on Twitter. First, we prepare a dataset of 147,028 tweets about COVID-19 posted during the first three waves of the pandemic, and then perform an analysis in three steps. In the first step, we train the LDA model and calculate the coherence values of the topics. We identify seven topics and report the ten most frequent words for each topic. In the second step, we analyze the proportion of tweets in each topic and report how these trends change over time. In the third step, we study spreading properties for each topic. The results show that all seven topics are evenly distributed across the three pandemic waves. The topic “vaccination” stands out with the change in percentage from 14.6% tweets in the first wave to 25.7% in the third wave. The obtained results contribute to a better understanding of pandemic communication in social media in Croatia.

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Acknowledgement

This work has been supported in part by the Croatian Science Foundation under the project IP-CORONA-04-2061, “Multilayer Framework for the Information Spreading Characterization in Social Media during the COVID-19 Crisis” (InfoCoV) and by University of Rijeka projects number uniri-drustv-18-20 and uniri-drustv-18-38. PKB is fully supported by Croatian Science Foundation under the project DOK-2021-02.

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Authors and Affiliations

  1. Faculty of Informatics and Digital Technologies, University of Rijeka, Radmile Matejčić 2, 51000, Rijeka, Croatia

    Petar Kristijan Bogović, Ana Meštrović & Sanda Martinčić-Ipšić

  2. Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejčić 2, 51000, Rijeka, Croatia

    Petar Kristijan Bogović, Ana Meštrović & Sanda Martinčić-Ipšić

Authors
  1. Petar Kristijan Bogović
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  2. Ana Meštrović
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  3. Sanda Martinčić-Ipšić
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Correspondence to Petar Kristijan Bogović .

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Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Audrius Lopata

  2. Kaunas University of Technology, Kaunas, Lithuania

    Daina Gudonienė

  3. Kaunas University of Technology, Kaunas, Lithuania

    Rita Butkienė

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Bogović, P.K., Meštrović, A., Martinčić-Ipšić, S. (2022). Topic Modeling for Tracking COVID-19 Communication on Twitter. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2022. Communications in Computer and Information Science, vol 1665. Springer, Cham. https://doi.org/10.1007/978-3-031-16302-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-16302-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16301-2

  • Online ISBN: 978-3-031-16302-9

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