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Conference Papers Year : 2018

Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations

Abstract

Targeted advertising has been subject to many privacy complaints from both users and policy makers. Despite this attention, users still have little understanding of what data the advertising platforms have about them and why they are shown particular ads. To address such concerns, Facebook recently introduced two transparency mechanisms: a "Why am I seeing this?" button that provides users with an explanation of why they were shown a particular ad (ad explanations), and an Ad Preferences Page that provides users with a list of attributes Facebook has inferred about them and how (data explanations). In this paper, we investigate the level of transparency provided by these two mechanisms. We first define a number of key properties of explanations and then evaluate empirically whether Facebook's explanations satisfy them. For our experiments, we develop a browser extension that collects the ads users receive every time they browse Facebook, their respective explanations, and the attributes listed on the Ad Preferences Page; we then use controlled experiments where we create our own ad campaigns and target the users that installed our extension. Our results show that ad explanations are often incomplete and sometimes misleading while data explanations are often incomplete and vague. Taken together, our findings have significant implications for users, policy makers, and regulators as social media advertising services mature.
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Dates and versions

hal-01955309 , version 1 (14-12-2018)

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Athanasios Andreou, Giridhari Venkatadri, Oana Goga, Krishna P Gummadi, Patrick Loiseau, et al.. Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations. NDSS 2018 - Network and Distributed System Security Symposium, Feb 2018, San Diego, United States. pp.1-15, ⟨10.14722/ndss.2018.23204⟩. ⟨hal-01955309⟩
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