Juan Miguel Carrascosa, Jakub Mikians, Rubén Cuevas, Vijay Erramilli, Nikolaos Laoutaris
Appeared in ACM CoNEXT 2015, Heidelberg, Germany
Online Behavioural targeted Advertising (OBA) has risen in prominence as a method to increase the effectiveness of online advertising. OBA operates by associating tags or labels to users based on their online activity and then using these labels to target them. This rise has been accompanied by privacy concerns from researchers, regulators and the press. In this paper, we present a novel methodology for measuring and understanding OBA in the online advertising market. We rely on training artificial online personas representing behavioural traits like ‘cooking’, ‘movies’, ‘motor sports’, etc. and build a measurement system that is automated, scalable and supports testing of multiple configurations. We observe that OBA is a frequent practice and notice that categories valued more by advertisers are more intensely targeted. In addition, we provide evidences showing that the advertising market targets sensitive topics (e.g, religion or health) despite the existence of regulation that bans such practices. We also compare the volume of OBA advertising for our personas in two different geographical locations (US and Spain) and see little geographic bias in terms of intensity of OBA targeting. Finally, we check for targeting with do-not-track (DNT) enabled and discover that DNT is not yet enforced in the web.
- In this paper we describe the methodology for the Auditing of Online Advertising Targeting design and developed as part of WP3 of TYPES.
- Using such methodology we discover that behavioural targeting is present in web advertising and that the online advertising ecosystem targets people based on sensible information
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