Modern digital news and media platforms deploy automated decision-making systems intensively, with positive as well as problematic results (Helberger, 2019). Search engines, personalised newsfeeds, content moderation systems and programmatic advertising all play integral roles in the modern media ecosystem (Nechushtai & Lewis, 2019). While commonplace, these technologies have created risks to democratic processes and social cohesion through the automated curation of personalised information and the algorithmic amplification of misinformation (Helberger, 2019). Critical responses to these systems highlight a lack of transparency around their design, operation, and impacts (Rieder & Hofmann, 2020). To address these concerns researchers are developing new auditing methods (Sandvig et al., 2014) to bring transparency to these systems and inform ongoing debate and inquiry.
Citizen science ‘data donation’ tools are one such methodological innovation, which like the popular algorithmic audit method (Sandvig et al., 2014), rely on direct one-on-one interaction with platforms, where results of those interactions are captured for comprehensive large-scale data analysis. These approaches rely on humans, or bots acting on behalf of a human, interacting with online services in a natural or pre-defined manner. Specific aspects of these interactions are then recorded for analysis. Unlike the audit which tends to rely on the generation of fake ‘personas’ as a locus of interaction with a web service, data donations rely on users volunteering authentic platform data generated through their regular use of these platforms, or from background ‘daemon’ processes designed to simulate everyday authentic web-based activities.
The data donation project that is the focus of this tool demo, the Australian Ad Observatory, successfully recruited two thousand volunteer Australian Facebook users throughout 2021/2022, who at time of writing have donated over 800,000 observations. These volunteers downloaded a custom browser plugin that captures Facebook advertisements they encounter during their regular use of the platform on their laptop or desktop computer. The Australian Ad Observatory extends pilot research by ProPublica and NYU in examining Facebook’s algorithmic advertising model (ProPublica, 2020). This plugin works by scanning users' Facebook News feeds, distinguishing, and capturing sponsored content (and only sponsored content). The content is then paired with de-identified demographic data of the donating user and sent to a central server. Data gathered through this tool is being studied to identify the potential for discriminatory advertising, and/or advertising that violates existing Australian regulatory codes or laws. The plugin has been developed to ensure compatibility with current plugin standards for the desktop versions of leading web browsers: Google Chrome, Mozilla Firefox, and Microsoft Edge.
1. Tool Demo: Improving
Platform Advertising
Observability Through
Browser-Based Citizen
Data Donation Methods
Prof Daniel Angus1, Prof Mark Andrejevic2,
Dr Abdul K. Obeid1, Xue Ying (Jane) Tan1,
A/Prof Nicholas Carah3, Prof Jean Burgess1
1Queensland University of Technology
2Monash University
3University of Queensland
3. “While observability incorporates similar regulatory
goals to transparency, it also deviates in important
respects, most importantly by understanding
accountability as a complex, dynamic ‘social relation’
(Bovens, 2007, p. 450), which is embedded in a specific
material setting.”
Rieder, B. & Hofmann, J. (2020). Towards platform observability. Internet
Policy Review, 9(4). https://doi.org/10.14763/2020.4.1535
Platform Observability
4.
5.
6.
7.
8. Click to edit theme subtitle style
Collecting Data Donations
20. Case Studies in:
Environmental claims / greenwashing
Consumer finance advertising
Advertising of unhealthy foods
Gambling advertising
Alcohol advertising
Political advertising
Empirical exploration of WAIST and
advertising targeting explanations
Mobile ad data capture
Future Work
https://www.admscentre.org.au/adobservatory/
Editor's Notes
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