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The Algorithmic Flow of Harmful Industries Advertising on Social Media Platforms

Datos Bibliográficos

ID22009112
AutoresN Carah (0000-0002-0378-1303, The University of Queensland), Maria-Gemma Brown (0000-0002-8892-6363, The University of Queensland), Amy Dobson (0000-0001-7268-9140), Amy S Dobson (0000-0003-4349-0815, Curtin University), B Robards (0000-0001-5031-1235, Australian Regenerative Medicine Institute)
Año2023
Fecha de publicación2023-03-29
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaAoIR Selected Papers of Internet Research (JOURNAL)
Identificadores de la revistaISSN: 2162-3317 • E-ISSN: 2162-3317
EditorialUniversity of Illinois Libraries (PUBLISHER)
DOI10.5210/spir.v2022i0.12985
OpenAlexW4365151857
IdiomaEN
Referencias citadas6

Advertising shapes our larger public culture but the typical experience of advertising is now confined to our private and algorithmically-customised social media feeds. In this project, with our partner VicHealth, we used a participatory digital method to work with 204 young Australians aged 18 to 25 to collect 5169 examples of alcohol, gambling and fast food advertising from their social media feeds. We analyse the collections of advertisements each participant sent us. The patterns across participants’ collections illustrate how social media platforms’ advertising models ‘learn’ to reflect and reproduce the identities and subject positions of participants. The collections of ads we see on social media are an important object of study because they reveal not just the symbolic content and targeting patterns of particular ads, but also because they illustrate how advertising on social media algorithmically-curates an immersive cultural experience. Our study demonstrates how social media continues the larger social role advertising plays in the construction and maintenance of consumer subjectivities. The algorithmic flow of advertising on social media is now the basis of our everyday engagement with advertising. We need to conceptualise advertising on social media not only using concepts of ‘targeting’ that imply the precise identification of our characteristics, but instead as a complex feedback loop between the refinement of ad content and themes, the data-driven optimization of audiences, and our reflexive and fluid identities, interests and aesthetic sensibilities

Advertising · Business · Citizen journalism · Contextual advertising · Native advertising · Online advertising · Social media · Sociology · The Internet · World Wide Web · Computer Science · Ethics and Social Impacts of AI · Innovative Human-Technology Interaction · Misinformation and Its Impacts · Artificial Intelligence

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    Taina Bucher•Information Communication & Society•2017

  • Alcohol marketing on social media

    Patricia Niland, Tim Mccreanor et al.•Addiction Research & Theory•2016

  • An exploration of alcohol advertising on social networking sites

    Amanda Atkinson, Amanda Marie Atkinson et al.•Addiction Research & Theory•2016

  • Automating the audience commodity

    Open Access•Lee Mcguigan•New Media & Society•2019

  • Shedding light on ‘dark’ ads

    Verity Trott, Nina Li et al.•Continuum•2021

  • Rhythmedia

    Open Access•Elinor Carmi•Theory Culture & Society•2020

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Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae