Understanding Interest-based Behavioural Targeted Advertising

Interest-based Behavioral Targeted (IBT) advertising has risen in prominence as a method to increase the effectiveness of online advertising. IBT operates by associating tags or labels to users based on their online activity and then using these labels to target them. It’s 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 IBT in the online advertising market. We rely on training artificial online personas representing behavioral traits like football enthusiast, affluent, recent parents etc. and build a measurement system that is automated, scalable and supports testing of multiple configurations. We observe that IBT advertising is a frequent practice and notice that some personas like Recent Parent are clearly more targeted than others such as Football Enthusiast. Furthermore, we compare the volume of IBT advertising for our personas in two different geographical locations (US and Spain) without observing any significant geographical bias in the utilization of IBT. Finally, we check for targeting with do-not-track (DNT) enabled and discovered that DNT is not yet enforced in the web.