Citizen science on twitter: Using data analytics to understand conversations and networks

MAZUMDAR, Suvodeep and THAKKER, D. (2020). Citizen science on twitter: Using data analytics to understand conversations and networks. Future Internet, 12 (12), 1-22.

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Open Access URL: https://www.mdpi.com/1999-5903/12/12/210 (Published version)
Link to published version:: https://doi.org/10.3390/fi12120210
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    Abstract

    © 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.

    Item Type: Article
    Identification Number: https://doi.org/10.3390/fi12120210
    Page Range: 1-22
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 17 Dec 2020 17:27
    Last Modified: 17 Dec 2020 17:30
    URI: http://shura.shu.ac.uk/id/eprint/27838

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