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

TL;DR

This paper introduces a scalable methodology using artificial personas to measure and analyze online behavioral advertising, revealing its prevalence, targeting of sensitive topics, minimal geographic bias, and the ineffectiveness of do-not-track signals.

Contribution

We develop a novel automated measurement system for OBA that enables large-scale analysis and comparison across regions and privacy settings.

Findings

OBA is a frequent advertising practice targeting sensitive topics.

Advertisers focus more on categories with higher value.

DNT signals are largely ignored by advertisers.

Abstract

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.