Manufacturing Dissent: A Mixed Methodological Analysis of Human Thought, Algorithmic Mediation, and Political Electioneering on Twitter

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dc.contributor.advisorPelkey, Jamin
dc.contributor.advisorWalsh Matthews, Stéphanie
dc.contributor.authorRicciardone, Sophia Marie
dc.date.accessioned2024-03-18T18:16:45Z
dc.date.available2024-03-18T18:16:45Z
dc.date.issued2024-03-16
dc.date.updated2024-03-16T10:40:31Z
dc.degree.disciplineCommunication & Culture, Joint Program with Toronto Metropolitan University
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractThe invisible entanglements of deep learning algorithms with political communication on social media platforms like Twitter have complicated political discourse and the formation of public opinion in the digital age. Consequently, as we engage with the content distributed on social media, it is difficult to know whether we are engaging with virtual peers or political bots. At the same time, the invisible interventions of bots also conceal the electioneering processes set in motion within political discourse on social media. Evidence has shown that because our minds cannot discern between tweets posted by human peers and those posted by bots, we intuitively engage with all tweets as though they were produced by social peers. Thus, the nature of our cognitive engagement with all tweets posted on social media conforms to the same social psychological principles that we engage when interacting with other social beings. Across this dissertation, I contend that the convergence of human thought, digital mediation, and digital electioneering creates distortions in logic on Twitter, resulting in a phenomenon I call botaganda. As the decussation of three different modes of reasoning infiltrate discourse within online spaces, the nature of discourse within public debate becomes convoluted, rendering human thought and public opinion vulnerable to the interference and manipulation of political actors. I aim to demonstrate that botaganda compromises the cogency and reliability of political communication in the digital age, but it is also the driving force behind the tenor of bipartisan incivility, politically motivated expression of moral outrage, and polarization of constituencies in the digital age. This dissertation also proposes that the political instrumentalization of deep learning algorithms on social media platforms to shape political discourse violates citizens’ fundamental rights to the freedom of thought, judgement, and conscience according to Section 2 the Canadian Charter of Rights and Freedoms.
dc.identifier.urihttps://hdl.handle.net/10315/41967
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectSocial psychology
dc.subjectMass communication
dc.subjectPolitical Science
dc.subject.keywordsDigital Culture
dc.subject.keywordsSocial Media
dc.subject.keywordsPolitics
dc.subject.keywordsElectioneering
dc.subject.keywordsAlgorithms
dc.subject.keywordsArtificial intelligence
dc.subject.keywordsCollaborative cognition
dc.subject.keywordsHjelmslev
dc.subject.keywordsSemiotics
dc.subject.keywordsInteraction alignment model
dc.subject.keywordsBargh
dc.subject.keywordsGarrod
dc.subject.keywordsPickering
dc.subject.keywordsPriming theory
dc.subject.keywordsSemantic priming
dc.subject.keywordsCognitive linguistics
dc.subject.keywordsCritical discourse analysis
dc.subject.keywordsPosner
dc.subject.keywordsSemantic contagion
dc.subject.keywordsPublic opinion
dc.subject.keywordsPosthumanism
dc.subject.keywordsHeuristics
dc.subject.keywordsMental schemas
dc.subject.keywordsMental frames
dc.subject.keywordsFraming theory
dc.subject.keywordsGlossematics
dc.subject.keywordsCorpus analysis
dc.subject.keywordsTwitter
dc.titleManufacturing Dissent: A Mixed Methodological Analysis of Human Thought, Algorithmic Mediation, and Political Electioneering on Twitter
dc.typeElectronic Thesis or Dissertation

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