Shedding light on ‘dark’ ads

Trott, Verity; Li, Nina; Fordyce, Robbie; Andrejevic, Mark

doi:10.1080/10304312.2021.1983258

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Ultimately, individuals cannot know how their data profiles are constituted: commercial data profiling and targeting are currently deployed in computational and legislative opacity (Trott et al, 2021). Although it is tempting to be overwhelmed by this opacity, it can also serve as an obstructive distraction or a ‘red herring’ for those looking to research algorithmic technology (Bucher, 2018).…”

Section: Researching Social Media Platformsmentioning

confidence: 99%

Algorithmic autobiographies and fictions: A digital method

Bishop

Kant

2023

The Sociological Review

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In this article, we outline an original, creative method for capturing the multifaceted ways in which digital technologies shape social life. We outline a framework for engaging participants in creative writing and drawing techniques to support ‘meeting and greeting’ their ‘algorithmic selves’. Algorithmic selves offer datafied reflections of individuals’ social media use, represented through platform approximated advertising categories. These categories include identities, such as ‘female’ or ‘male’, and marketing interests as ‘dog lovers’, ‘first time buyers’ or ‘feminists’. Our method builds on Les Back’s calls for ‘a more artful form of sociology’ that is able to think with technology. By using algorithmic selves to mobilise creative enquiry in this way, we argue that researchers can better discern how technology users make sense of their data, the ways in which identity can be co-constructed by social media platforms, and how our interactions with technology ultimately shape social lives in meaningful and highly affective ways. Our method offers a craft-based framework for understanding imaginations, associations and connections with data profiling, and making these understandings available for participant reflection and researcher analysis. This method can also support research participants in taking creative ownership and building agency around their interactions with social media platforms.

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Section: Researching Social Media Platformsmentioning

confidence: 99%

Algorithmic autobiographies and fictions: A digital method

Bishop

Kant

2023

The Sociological Review

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“…Seeing an algorithmically-curated flow of advertising as we scroll through our social media feeds is now an ordinary part of our digital lives (Bucher 2017, Carmi 2020, Trott et al 2021. While researchers have examined both the targeted advertising and promotional brand cultures of social media platforms (Banet-Weiser 2012, Bishop 2018, McGuigan 2019, in this paper we argue for the need to investigate the customised flow of advertising itself.…”

Section: Introductionmentioning

confidence: 96%

The Algorithmic Flow of Harmful Industries Advertising on Social Media Platforms

Carah

Brown

Dobson

et al. 2023

SPIR

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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.

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“…Again, without independent passive monitoring systems in place to capture the ephemeral content that actually reaches users, the manipulation of ads by platforms like Facebook and Amazon would be impossible to detect [ 2 , 51 , cf. 64 , 65 ].…”

Section: Introductionmentioning

confidence: 99%

What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME)

Epstein¹,

Tyagi²,

Wang³

2023

PLoS ONE

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The internet has made possible a number of powerful new forms of influence, some of which are invisible to users and leave no paper trails, which makes them especially problematic. Some of these effects are also controlled almost exclusively by a small number of multinational tech monopolies, which means that, for all practical purposes, these effects cannot be counteracted. In this paper, we introduce and quantify an effect we call the Targeted Messaging Effect (TME)–the differential impact of sending a consequential message, such as a link to a damning news story about a political candidate, to members of just one demographic group, such as a group of undecided voters. A targeted message of this sort might be difficult to detect, and, if it had a significant impact on recipients, it could undermine the integrity of the free-and-fair election. We quantify TME in a series of four randomized, controlled, counterbalanced, double-blind experiments with a total of 2,133 eligible US voters. Participants were first given basic information about two candidates who ran for prime minister of Australia in 2019 (this, to assure that our participants were “undecided”). Then they were instructed to search a set of informational tweets on a Twitter simulator to determine which candidate was stronger on a given issue; on balance, these tweets favored neither candidate. In some conditions, however, tweets were occasionally interrupted by targeted messages (TMs)–news alerts from Twitter itself–with some alerts saying that one of the candidates had just been charged with a crime or had been nominated for a prestigious award. In TM groups, opinions shifted significantly toward the candidate favored by the TMs, and voting preferences shifted by as much as 87%, with only 2.1% of participants in the TM groups aware that they had been viewing biased content.

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Shedding light on ‘dark’ ads

Cited by 3 publications

References 7 publications

Algorithmic autobiographies and fictions: A digital method

Algorithmic autobiographies and fictions: A digital method

The Algorithmic Flow of Harmful Industries Advertising on Social Media Platforms

What would happen if twitter sent consequential messages to only a strategically important subset of users? A quantification of the Targeted Messaging Effect (TME)

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