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Online Behavioural targeted Advertising (OBA) has risen in prominence as amethod to increase the effectiveness of online advertising. OBA operates byassociating tags or labels to users based on their online activity and thenusing these labels to target them. This rise has been accompanied by privacyconcerns from researchers, regulators and the press. In this paper, we presenta novel methodology for measuring and understanding OBA in the onlineadvertising market. We rely on training artificial online personas representingbehavioural traits like 'cooking', 'movies', 'motor sports', etc. and build ameasurement system that is automated, scalable and supports testing of multipleconfigurations. We observe that OBA is a frequent practice and notice thatcategories valued more by advertisers are more intensely targeted. In addition,we provide evidences showing that the advertising market targets sensitivetopics (e.g, religion or health) despite the existence of regulation that banssuch practices. We also compare the volume of OBA advertising for our personasin two different geographical locations (US and Spain) and see littlegeographic bias in terms of intensity of OBA targeting. Finally, we check fortargeting with do-not-track (DNT) enabled and discovered that DNT is not yetenforced in the web.

I Always Feel Like Somebody's Watching Me. Measuring Online Behavioural Advertising