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Article published In:
Journal of Language and Politics
Vol. 22:4 (2023) ► pp.458–484

Emergent Twitter publics through political scandal

An example from the Covid-19 Crisis in the UK

ORCID logo | University of Strathclyde
| University of Sunderland
| University of Strathclyde
This study examines how emergent Twitter publics are organised and engage with political scandal and personalisation during Covid-19 in the UK. The analysis is centred on a series of media events around Chief Adviser to the then-UK Prime Minister, running from May 2020 to May 2021. The samples comprises original tweets that contain key hashtags, amounting to 38,326 items. These are subject to topic model analysis to identify semantic fields, before using critical discourse analysis. We find hashtags help constitute emergent Twitter publics, and that tweets follow conversational patterns and conspire in tactics of intertextuality. Dissention to government conduct engages resourcefully with the affordances of Twitter: constituting publics, shaping discourse, and articulating with parallel discussions on political performance. Further, a computational approach can systematise the identification of domains of discourse and relevant lexical sets, providing an evidence-based understanding of even novel and emergent political discourses in online discussion.
Keywords: computational discourse analysis, political personalisation, Twitter, semantic fields, topic models
Article outline
  • 1.Introduction
  • 2.Personalisation and political scandal
  • 3.The analysis of Twitter
  • 4.The use of hashtags
  • 5.Semantic fields
  • 6.Gathering the data
  • 7.Analytical approach
    • 7.1Coherence and prevalence of topics
  • 8.Discussion
    • 8.1Joining the Fray: Conversationalisation and intertextuality
    • 8.2Semantic fields and intertextuality: The use of ‘ladies and gentlemen’
    • 8.3Hashtags and Punning: Creative expansions
    • 8.4Towards shared creative descriptive practices amongst publics
  • 9.Conclusion
  • References
Published online: 9 May 2023
DOI logo
https://doi.org/10.1075/jlp.22028.rat
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