#IStandWithDan versus #DictatorDan: the polarised dynamics of Twitter discussions about Victoria's COVID-19 restrictions
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Description
In this article, we examine two interrelated hashtag campaigns that formed in response to the Victorian State Government’s handling of Australia’s most significant COVID-19 second wave of mid-to-late 2020. Through a mixed-methods approach that includes descriptive statistical analysis, qualitative content analysis, network analysis, computational sentiment analysis and social bot detection, we reveal how a small number of hyper-partisan pro- and anti-government campaigners were able to mobilise ad hoc communities on Twitter, and – in the case of the anti-government hashtag campaign – co-opt journalists and politicians through a multi-step flow process to amplify their message. Our comprehensive analysis of Twitter data from these campaigns offers insights into the evolution of political hashtag campaigns, how actors involved in these specific campaigns were able to exploit specific dynamics of Twitter and the broader media and political establishment to progress their hyper-partisan agendas, and the utility of mixed-method approaches in helping render the dynamics of such campaigns visible.
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ID Code: | 207260 | ||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||
Refereed: | Yes | ||||||||
ORCID iD: |
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Additional Information: | Funding Information: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is funded by an internal grant from the Digital Media Research Centre, Queensland University of Technology (QUT). | ||||||||
Measurements or Duration: | 22 pages | ||||||||
Keywords: | clicktivism, Coronavirus, COVID-19, disinformation, hashtag activism, misinformation, multi-step flow model, social media, Twitter | ||||||||
DOI: | 10.1177/1329878X20981780 | ||||||||
ISSN: | 1329-878X | ||||||||
Pure ID: | 74442940 | ||||||||
Divisions: | Current > Research Centres > Centre for Data Science Current > Research Centres > Digital Media Research Centre Current > QUT Faculties and Divisions > Academic Division Current > QUT Faculties and Divisions > Faculty of Science Current > QUT Faculties and Divisions > Faculty of Creative Industries, Education & Social Justice Current > Schools > School of Communication |
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Funding Information: | The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is funded by an internal grant from the Digital Media Research Centre, Queensland University of Technology (QUT). | ||||||||
Copyright Owner: | © The Author(s) 2020. | ||||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||
Deposited On: | 12 Jan 2021 06:37 | ||||||||
Last Modified: | 04 Aug 2024 23:25 |
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