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Identifying Early Opinion Leaders on COVID-19 on Twitter

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HCI International 2021 - Late Breaking Papers: Design and User Experience (HCII 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13094))

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Abstract

This study aims to empirically identify opinion leaders on Twitter from the lens of Innovation Diffusion theory. We analyzed pandemic-specific tweets from casual users as well as from the US President to map their conversation for the purpose of finding opinion leaders over a three month period at the onset of the pandemic. By applying network analysis following with cluster enrichment as well as sentiment analysis, we recognize potential thought leaders, but we could not find strong evidence for opinion leaders according to the Innovation Diffusion theory. We interpret that users tweet for two different purposes - tweets to elicit agreement and tweets to elicit debate.

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Notes

  1. 1.

    IDs for who was involved to the ConvID with his/her tweet.

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Acknowledgement

A big thanks to Rui Yang from The University of Nebraska at Omaha for providing a dataset of English-speaking lay users’ conversations which were extracted from the Twitter platform. For citations of references, we prefer the use of square brackets and consecutive numbers. Citations using labels or the author/year convention are also acceptable.

Author information

Authors and Affiliations

  1. University of Nebraska at Omaha, Omaha, 68182, USA

    Zahra Hatami, Margeret Hall & Neil Thorne

Authors
  1. Zahra Hatami
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  2. Margeret Hall
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  3. Neil Thorne
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Corresponding author

Correspondence to Zahra Hatami .

Editor information

Editors and Affiliations

  1. University of Creteand Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

  2. Department of Design, Federal University of Pernambuco, Recife, Brazil

    Marcelo M. Soares

  3. World Usability Day and Brandeis University, Newton Center, MA, USA

    Elizabeth Rosenzweig

  4. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  5. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  6. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  7. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  8. Computer and Information Sciences, Towson University, Towson, MD, USA

    Gabriele Meiselwitz

  9. DePaul University, Chicago, IL, USA

    Xiaowen Fang

  10. San Jose State University, San Jose, CA, USA

    Abbas Moallem

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Hatami, Z., Hall, M., Thorne, N. (2021). Identifying Early Opinion Leaders on COVID-19 on Twitter. In: Stephanidis, C., et al. HCI International 2021 - Late Breaking Papers: Design and User Experience. HCII 2021. Lecture Notes in Computer Science(), vol 13094. Springer, Cham. https://doi.org/10.1007/978-3-030-90238-4_20

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  • DOI: https://doi.org/10.1007/978-3-030-90238-4_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90237-7

  • Online ISBN: 978-3-030-90238-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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