Miranda Wei, Madison Stamos, Sophie Veys, Nathan Reitinger, Justin Goodman, Margot Herman, Dorota Filipczuk, Ben Weinshel, Michelle L. Mazurek, Blase Ur
Proceedings of the 29th USENIX Security Symposium. Online, August 2020. (USENIX Security '20)
Although targeted advertising has drawn signiﬁcant attention from privacy researchers, many critical empirical questions remain. In particular, only a few of the dozens of targeting mechanisms used by major advertising platforms are well understood, and studies examining users’ perceptions of ad targeting often rely on hypothetical situations. Further, it is unclear how well existing transparency mechanisms, from data-access rights to ad explanations, actually serve the users they are intended for. To develop a deeper understanding of the current targeting advertising ecosystem, this paper engages 231 participants’ own Twitter data, containing ads they were shown and the associated targeting criteria, for measure- ment and user study. We ﬁnd many targeting mechanisms ignored by prior work — including advertiser-uploaded lists of specific users, lookalike audiences, and retargeting campaigns — are widely used on Twitter. Crucially, participants found these understudied practices among the most privacy invasive. Participants also found ad explanations designed for this study more useful, more comprehensible, and overall more preferable than Twitter’s current ad explanations. Our ﬁndings underscore the beneﬁts of data access, characterize unstudied facets of targeted advertising, and identify potential directions for improving transparency in targeted advertising.