  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 discovered that DNT is not yet
enforced in the web.
