What Twitter Knows: Characterizing Ad Targeting Practices, User Perceptions, and Ad Explanations Through Users' Own Twitter Data
Miranda Wei, University of Washington / University of Chicago; Madison Stamos and Sophie Veys, University of Chicago; Nathan Reitinger and Justin Goodman, University of Maryland; Margot Herman, University of Chicago; Dorota Filipczuk, University of Southampton; Ben Weinshel, University of Chicago; Michelle L. Mazurek, University of Maryland; Blase Ur, University of Chicago
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 measurement 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.
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