Stop tracking me Bro! Differential Tracking of User Demographics on Hyper-Partisan Websites
Pushkal Agarwal, Sagar Joglekar, Panagiotis Papadopoulos, Nishanth Sastry, Nicolas Kourtellis
Abstract
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 websites tend to track users more intensely than left-leaning, depending on user demographics, using both cookies and cookie synchronization methods and leading to more costly delivered ads.
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Install the CLIlune papers fulltext 23068c72-0058-446c-8112-d8655ea43899Cited by top-tier papers6
- User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent to Track UsersEmmanouil Papadogiannakis, Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. MarkatosWWW 2021 · 99 citations
- Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News SitesEmmanouil Papadogiannakis, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas KourtellisWWW 2023 · 39 citations
- The Hitchhiker's Guide to Facebook Web Tracking with Invisible Pixels and Click IDsPaschalis Bekos, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas KourtellisWWW 2023 · 24 citations
- GraphNLI: A Graph-based Natural Language Inference Model for Polarity Prediction in Online DebatesVibhor Agarwal, Sagar Joglekar, Anthony P. Young, Nishanth SastryWWW 2022 · 23 citations
- Before & After: The Effect of EU's 2022 Code of Practice on DisinformationEmmanouil Papadogiannakis, Panagiotis Papadopoulos, Nicolas Kourtellis, Evangelos P. MarkatosWWW 2025 · 2 citations
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