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Using Topic Modeling and NLP Tools for Analyzing Long Covid Coverage by French Press and Twitter

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Intelligent Sustainable Systems (WorldS4 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 817))

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Abstract

During the current COVID-19 pandemic, a specific form of the disease called “Long Covid” was the subject of debates and contradictory—even conflicting—positions of the stakeholders, until its institutional and public recognition in 2022. The objectives of this study are : (1) analyzing the modalities of construction and progressive visibility of the category “Long Covid” in the media public sphere and in Twitter; (2) to propose a methodology based on topic modeling and NLP tools to conduct a comparative analysis between newspapers and Twitter coverage.

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Acknowledgements

This study was partially funded by the Institute of Public Health Sciences (ISSPAM—Aix-Marseille University), the Labex DRIIHM (ANR-11-LABX-0010, OHM Pima County) and iGLOBES (UMI 3157).

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Authors and Affiliations

  1. Centre Norbert Elias, Aix Marseille University, Marseille, France

    Brigitte Juanals

  2. MoDyCo, Paris Nanterre University, Nanterre, France

    Jean-Luc Minel

Authors
  1. Brigitte Juanals
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  2. Jean-Luc Minel
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Corresponding author

Correspondence to Brigitte Juanals .

Editor information

Editors and Affiliations

  1. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh Jat

  3. Computer Science and Engineering, Sri Aurobindo Institute of Technology, Indore, Madhya Pradesh, India

    Durgesh Kumar Mishra

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Juanals, B., Minel, JL. (2024). Using Topic Modeling and NLP Tools for Analyzing Long Covid Coverage by French Press and Twitter. In: Nagar, A.K., Jat, D.S., Mishra, D.K., Joshi, A. (eds) Intelligent Sustainable Systems. WorldS4 2023. Lecture Notes in Networks and Systems, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-99-7886-1_15

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