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Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks

Author

Listed:
  • Suvodeep Mazumdar

    (Information School, The University of Sheffield, Sheffield S10 2TN, UK
    These authors contributed equally to this work.)

  • Dhavalkumar Thakker

    (Department of Computer Science, University of Bradford, Bradford BD7 1DP, UK
    These authors contributed equally to this work.)

Abstract

This paper presents a long-term study on how the public engage with discussions around citizen science and crowdsourcing topics. With progress in sensor technologies and IoT, our cities and neighbourhoods are increasingly sensed, measured and observed. While such data are often used to inform citizen science projects, it is still difficult to understand how citizens and communities discuss citizen science activities and engage with citizen science projects. Understanding these engagements in greater depth will provide citizen scientists, project owners, practitioners and the generic public with insights around how social media can be used to share citizen science related topics, particularly to help increase visibility, influence change and in general and raise awareness on topics. To the knowledge of the authors, this is the first large-scale study on understanding how such information is discussed on Twitter, particularly outside the scope of individual projects. The paper reports on the wide variety of topics (e.g., politics, news, ecological observations) being discussed on social media and a wide variety of network types and the varied roles played by users in sharing information in Twitter. Based on these findings, the paper highlights recommendations for stakeholders for engaging with citizen science topics.

Suggested Citation

  • Suvodeep Mazumdar & Dhavalkumar Thakker, 2020. "Citizen Science on Twitter: Using Data Analytics to Understand Conversations and Networks," Future Internet, MDPI, vol. 12(12), pages 1-22, November.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:210-:d:451652
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    References listed on IDEAS

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    Cited by:

    1. Marialisa Scatá & Barbara Attanasio & Aurelio La Corte, 2021. "Cognitive Profiling of Nodes in 6G through Multiplex Social Network and Evolutionary Collective Dynamics," Future Internet, MDPI, vol. 13(5), pages 1-17, May.
    2. Abdulelah A. Alghamdi & Margaret Plunkett, 2021. "The Perceived Impact of Social Networking Sites and Apps on the Social Capital of Saudi Postgraduate Students: A Case Study," Future Internet, MDPI, vol. 13(1), pages 1-23, January.

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