Stop tracking me Bro! Differential Tracking of User Demographics on Hyper-Partisan Websites

TY - CHAP

T1 - Stop tracking me Bro! Differential Tracking of User Demographics on Hyper-Partisan Websites

AU - Agarwal, Pushkal

AU - Joglekar, Sagar Prakash

AU - Sastry, Nishanth Ramakrishna

AU - Papadapoulos, Panagiotis

AU - Kourtellis, Nicolas

PY - 2020/1/10

Y1 - 2020/1/10

N2 - Websites with hyper-partisan, left or right-leaning focus offer content that is typically biased towards the expectations of their target audience. Such content often polarizes users, who are repeatedly primed to specific (extreme) content, usually reflecting hard party lines on political and socio-economic topics. Though this polarization has been extensively studied with respect to content, it is still unknown how it associates with the online tracking experienced by browsing users, especially when they exhibit certain demographic characteristics. For example, it is unclear how such websites enable the ad-ecosystem to track users based on their gender or age. In this paper, we take a first step to shed light and measure such potential differences in tracking imposed on users when visiting specific party-line’s websites. For this, we design and deploy a methodology to systematically probe such websites and measure differences in user tracking. This methodology allows us to create user personas with specific attributes like gender and age and automate their browsing behavior in a consistent and repeatable manner. Thus, we systematically study how personas are being tracked by these websites and their third parties, especially if they exhibit particular demographic properties. Overall, we test 9 personas on 556 hyper-partisan websites and find that right-leaning sites tend to track users more intensely than left-leaning, always depended on user demographics, and using both cookies and cookie synchronization methods, leading to more costly delivered ads.

AB - Websites with hyper-partisan, left or right-leaning focus offer content that is typically biased towards the expectations of their target audience. Such content often polarizes users, who are repeatedly primed to specific (extreme) content, usually reflecting hard party lines on political and socio-economic topics. Though this polarization has been extensively studied with respect to content, it is still unknown how it associates with the online tracking experienced by browsing users, especially when they exhibit certain demographic characteristics. For example, it is unclear how such websites enable the ad-ecosystem to track users based on their gender or age. In this paper, we take a first step to shed light and measure such potential differences in tracking imposed on users when visiting specific party-line’s websites. For this, we design and deploy a methodology to systematically probe such websites and measure differences in user tracking. This methodology allows us to create user personas with specific attributes like gender and age and automate their browsing behavior in a consistent and repeatable manner. Thus, we systematically study how personas are being tracked by these websites and their third parties, especially if they exhibit particular demographic properties. Overall, we test 9 personas on 556 hyper-partisan websites and find that right-leaning sites tend to track users more intensely than left-leaning, always depended on user demographics, and using both cookies and cookie synchronization methods, leading to more costly delivered ads.

M3 - Conference paper

BT - Proceedings of the The Web Conference (WWW 2020)

PB - International World Wide Web Conferences Steering Committee

ER -