#IStandWithDan versus #DictatorDan: the polarised dynamics of Twitter discussions about Victoria’s COVID-19 restrictions

Abstract

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.

Exploring the effect of streamed social media data variations on social network analysis

“…2020 ; Weber and Neumann 2020 ; Graham et al. 2020 ). Key for future social media research is to develop processes for repeatable analysis, including access to common datasets, both of which underpin the practice of benchmarking.…”

Die Rolle der Medien in der COVID-19-Pandemie

“…Darüber hinaus hat eine qualitative Inhaltsanalyse Beispiele für desinformative visuelle Darstellungen untersucht [76]. Analysen von Social Media in Australien zeigen, wie eine kleine Anzahl von Aktivistinnen und Aktivisten Ad-hoc-Communities auf Twitter mobilisieren und regierungsfeindliche Hashtag-Kampagnen starten können -zum Teil gemeinsam mit potenziell sympathisierenden Journalistinnen, Journalisten, Politikerinnen, Politikern sowie hochaktiven Meinungsführerinnen, Meinungsführern und ihren Anhängern, die Desinformationen, kritische Meinungen und Rhetoriken verbreiten [77].…”

Lots of bots or maybe nots: A process for detecting bots in social media research

“…Social media data have become an alluring source of insight into the mindsets of the general population by providing a relatively cheap, quick, and easy-to-access source of data (Graham et al, 2020). These benefits align with shrinking corporate and academic budgets and time pressures to turnaround projects, particularly exacerbated by COVID-19.…”

“…Such filtering may happen at two levels-first at a thematic level, to exclude comments that do not fit the researcher's criteria, and second, to exclude those that prima facie appear authentic but may be fabricated. Recent studies (Crabb et al, 2015;Freelon et al, 2020;Graham et al, 2020) have highlighted the concern of highly organized online activists who can influence and contaminate data with the use of bots (automated content creation tools), spam (mass communication messaging), and "sockpuppets" (a fake account designed to distort and manipulate public opinion). Despite acknowledging the challenges in using social media data, few studies have explored this process and how it may be applied effectively in practice (Graham et al, 2020).…”

“…Recent studies (Crabb et al, 2015;Freelon et al, 2020;Graham et al, 2020) have highlighted the concern of highly organized online activists who can influence and contaminate data with the use of bots (automated content creation tools), spam (mass communication messaging), and "sockpuppets" (a fake account designed to distort and manipulate public opinion). Despite acknowledging the challenges in using social media data, few studies have explored this process and how it may be applied effectively in practice (Graham et al, 2020). This article, therefore, provides a structured approach that industry and academic researchers can apply when using social media data.…”