Over its first decade Instagram became central to Facebook’s dominance of audience and advertising markets. In this article, we critically examine how marketing and advertising trade press documented the development of advertising and retail on the platform. Instagram’s platformization involved formalizing relationships among users, influencers, creators, advertisers, retailers, and analytics services. The development of advertising and retail on the platform was characterized by open-ended third-party experimentation and innovation, which was gradually incorporated into, and controlled by, the platform. Advertisers and marketers increasingly approach the platform not just as an advertising service, but an end-to-end advertising, analytics and retail infrastructure. While much attention has been given to the promotional and influencer culture of Instagram, advertisers and marketers saw it as an opportunity to integrate advertising with retail. We argue that Instagram has platformized practices of looking and buying historically associated with department stores, malls, home shopping, and catalogs.
BackgroundPsoriatic arthritis (PsA) is an inflammatory arthritis associated with psoriasis with a genetic component estimated to be larger than psoriasis alone. Recent studies have identified PsA-specific loci that begin to explain this increased burden, for example, amino acids in HLA-B and our own Immunochip study reported evidence for PsA-specific risk at chromosome 5q31 and IL23R.MethodsA total of 14 single nucleotide polymorphisms (SNP) were selected from our recent Immunochip study (P <1x10-4) to validate in 3,139 PsA cases and 11,078 controls from the UK, Republic of Ireland, Germany, Australia, Sweden and Italy using the Life Technologies QuantStudio genotyping platform. Association testing was performed using PLINK. For loci not previously reported for psoriasis we compare effect sizes using multinomial logistic regression, performed in Stata, and directly compare PsA genotypes from Immunochip (n=1,936) to the psoriasis WTCCC2 study (excluding known PsA, n=1,784). To control for phenotype misclassification with rheumatoid arthritis (RA), we include a genetic risk sore comprised of the 41 RA susceptibility reported in the RA Immunochip study as a covariate and re-analysed the PsA Immunochip.ResultsWe find genome-wide significant association to rs2476601, mapping to the gene PTPN22 (P=1.49x10-9, OR =1.32). There was no evidence for association to rs2476601 in the psoriasis WTCCC2 cohort (P=0.34) and the effect estimates were significantly different between PsA and psoriasis (P=3.2x10-4). Direct comparison of genotypes for PsA and psoriasis found significant association to an increased risk of PsA (P=4.4x10-4, OR=1.3). The association to PTPN22 in the PsA Immunochip data was not affected by the inclusion of the RA-GRS as a covariate. In addition, we find genome-wide significant association to the previously reported psoriasis risk loci; NOS2 (rs4795067, P=5.27x10-9). No other SNPs reached genome-wide significance in the combined dataset.ConclusionsFor the first time, we report genome-wide significant association of PTPN22 (rs2476601) to PsA susceptibility. The risk allele (A) and direction of effect are consistent with previous reports for RA and type I diabetes, but opposite of that reported for Crohn's disease. We provide evidence that this is a PsA-specific risk locus as no association to psoriasis was observed in the WTCCC2 cohort and the effect estimates are significantly different between PsA and psoriasis when compared in multinomial logistic regression.Disclosure of InterestNone declared
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.
This paper uses a novel combination of computational and qualitative methods called ‘critical simulation’ to explore the interplay between everyday image-making practices and the algorithmic architecture of Instagram. The paper aims to understand the capacity of machine vision systems to recognise and reproduce the diverse vernacular aesthetics and affects associated with particular scenes on Instagram - in this case, drawing on a case study of Instagram’s ‘-cores’ hashtags. We used our purpose-built machine vision system to undertake unsupervised clusterings of a sample of the 359,150 images associated with a curated set of 60 ‘-cores’ hashtags, which we collected following a period of immersive qualitative investigation of the -cores phenomenon on the platform itself during 2021. We assess the extent to which the system's clusters align with the different -cores hashtags under which the images were originally posted, and then undertake a close cultural analysis of the clusters, reading them through the lens of our existing knowledge of the -core hashtags. This enables us to speculate on how the platforms’ machine vision logics might play a role in shaping Instagram’s platform aesthetics, and on internet culture more broadly.
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