Observing "tuned" advertising on digital platforms - Nicholas Carah; Lauren Hayden; Maria-Gemma Brown; Daniel Angus; Aimee Brownbill; Kiah Hawker; Xue Ying Tan; Amy Dobson; Brady Robards (Internet Policy Review, 2024)
The hyper-targeted advertising that emerged on digital platforms over the past two decades is now more productively understood as tuned advertising, a dynamic and unfolding process where ads are continuously algorithmically "optimised" to users in real time. Following Rieder and Hofmann (2020), we aim to develop a framework for the "conditions for the practice of observing" algorithmically-tuned digital advertising. We draw on our research across the Australian Ad Observatory and a multi-year research project on digital alcohol advertising. Across these projects we build customised tools to collect ads from platform ad libraries and through data donation from citizen scientists. We argue that the power of digital advertising is increasingly located in its capacity to tune. Platforms' ad transparency tools draw our attention to ads, but we need to develop the capacity to observe the dynamic socio-technical process of tuning. We conceptualise and present visualisations of "tuned sequences" of ads, as an alternative to "libraries" of ads. We argue that developing the capacity to observe these tuned sequences better articulates the mode of observation required to develop the forms of public understanding and accountability both civil society organisations and researchers are looking for.
Keywords: 300; Digital platforms; Advertising; Observability; Algorithms; Harmful industries