ABSTRACT
Much of what a user sees browsing the internet, from ads to search results, is targeted or personalized by algorithms that have made inferences about that user. Prior work has documented that users find such targeting simultaneously useful and creepy. We begin unpacking these conflicted feelings through two online studies. In the first study, 306 participants saw one of ten explanations for why they received an ad, reflecting prevalent methods of targeting based on demographics, interests, and other factors. The type of interest-based targeting described in the explanation affected participants' comfort with the targeting and perceptions of its usefulness. We conducted a follow-up study in which 237 participants saw ten interests companies might infer. Both the sensitivity of the interest category and participants' actual interest in that topic significantly impacted their attitudes toward inferencing. Our results inform the design of transparency tools.
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- Gunes Acar, Marc Juarez, Nick Nikiforakis, Claudia Diaz, Seda G¨ urses, Frank Piessens, and Bart Preneel. 2013. FPDetective: Dusting the web for fingerprinters. In Proc. CCS. Google ScholarDigital Library
- Lalit Agarwal, Nisheeth Shrivastava, Sharad Jaiswal, and Saurabh Panjwani. 2013. Do not embarrass: Re-examining user concerns for online tracking and advertising. In Proc. SOUPS. Google ScholarDigital Library
- Julia Angwin, Madeleine Varner, and Ariana Tobin. September 14, 2017. Facebook Enabled Advertisers to Reach 'Jew Haters'. ProPublica. https://www.propublica.org/article/facebookenabled-advertisers-to-reach-jew-haters.Google Scholar
- Rebecca Balebako, Pedro Leon, Richard Shay, Blase Ur, Yang Wang, and L. Cranor. 2012. Measuring the effectiveness of privacy tools for limiting behavioral advertising. In Proc. W2SP.Google Scholar
- Farah Chanchary, Yomna Abdelaziz, and Sonia Chiasson. April 22, 2017. Privacy Concerns Amidst OBA and the Need for Alternative Models. To appear in IEEE Internet Computing. http://chorus.scs.carleton.ca/wp/wp-content/ papercite-data/pdf/chanchary2017sharing-ic.pdfGoogle Scholar
- Cliqz. Ghostery. Retrieved September 2017 from http://www.ghostery.comGoogle Scholar
- Rena Coen, Emily Paul, Pavel Vanegas, Alethea Lange, and G.S. Hans. 2016. A User-Centered Perspective on Algorithmic Personalization. University of California, Berkeley MIMS Final Project. https://www.ischool. berkeley.edu/projects/2016/user-centeredperspective-algorithmic-personalization.Google Scholar
- Amit Datta, Michael Carl Tschantz, and Anupam Datta. 2015. Automated experiments on ad privacy settings. PoPETS 1 (2015), 92--112.Google Scholar
- ECUAD. Visualizing Lightbeam. Retrieved September 2017 from http://research.ecuad.ca/lightbeamGoogle Scholar
- EFF. Privacy Badger. Retrieved September 2017 from http://www.eff.org/privacybadgerGoogle Scholar
- Steven Englehardt and Arvind Narayanan. 2016. Online Tracking: A 1-million-site Measurement and Analysis. In Proc. CCS. Google ScholarDigital Library
- Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan Mayer, Arvind Narayanan, and Edward W. Felten. 2015. Cookies that give you away: The surveillance implications of web tracking. In Proc. WWW. Google ScholarDigital Library
- Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik. 2016. First I like it, then I hide it: Folk Theories of Social Feeds. In Proc. CHI. Google ScholarDigital Library
- Motahhare Eslami, Aimee Rickman, Kristen Vaccaro, Amirhossein Aleyasen, Andy Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. 2015. "I always assumed that I wasn't really that close to {her}": Reasoning about Invisible Algorithms in News Feeds. In Proc. CHI. Google ScholarDigital Library
- Facebook. Your ad preferences. Retrieved September 2017 from https://www.facebook.com/ads/ preferences/?entry_product=ad_settings_screenGoogle Scholar
- Google. Ad settings. Retrieved September 2017 from https://adssettings.google.com/authenticatedGoogle Scholar
- Google. Add multiple topics with topic codes. Retrieved September 2017 from https://support.google.com/ adwords/answer/156178?authuser=0#topic_codesGoogle Scholar
- Google. Advertising Policies Help. Retrieved September 2017 from https://support.google.com/ adwordspolicy/answer/143465Google Scholar
- Google. Topics used for personalized ads. Retrieved September 2017 from https: //support.google.com/ads/answer/2842480?hl=enGoogle Scholar
- Kashmir Hill. February 16, 2012. How Target figured out a teen girl was pregnant before her father did. Forbes. http://www.forbes.com/sites/kashmirhill/2012/ 02/16/how-target-figured-out-a-teen-girl-waspregnant-before-her-father-did/#2caffa3a34c6.Google Scholar
- Alex Kantrowitz. September 15, 2017. Google Allowed Advertisers To Target People Searching Racist Phrases. BuzzFeed. https://www.buzzfeed.com/alexkantrowitz/googleallowed-advertisers-to-target-jewish-parasiteblack.Google Scholar
- Balachander Krishnamurthy, Delfina Malandrino, and Craig E. Wills. 2007. Measuring Privacy Loss and the Impact of Privacy Protection in Web Browsing. In Proc. SOUPS. Google ScholarDigital Library
- Balachander Krishnamurthy and Craig Wills. 2009. Privacy Diffusion on the Web: A Longitudinal Perspective. In Proc. WWW. Google ScholarDigital Library
- Mathias Lecuyer, Guillaume Ducoffe, Francis Lan, Andrei Papancea, Theofilos Petsios, Riley Spahn, Augustin Chaintreau, and Roxana Geambasu. 2014. XRay: Enhancing the Web's Transparency with Differential Correlation. In Proc. USENIX Security. Google ScholarDigital Library
- Mathias Lecuyer, Riley Spahn, Yannis Spiliopolous, Augustin Chaintreau, Roxana Geambasu, and Daniel Hsu. 2015. Sunlight: Fine-grained Targeting Detection at Scale with Statistical Confidence. In Proc. CCS. Google ScholarDigital Library
- Pedro Leon, Blase Ur, Richard Shay, Yang Wang, Rebecca Balebako, and Lorrie Cranor. 2012. Why Johnny can't opt out: A usability evaluation of tools to limit online behavioral advertising. In Proc. CHI. Google ScholarDigital Library
- Pedro G. Leon, Blase Ur, Yang Wang, Manya Sleeper, Rebecca Balebako, Richard Shay, Lujo Bauer, Mihai Christodorescu, and Lorrie Faith Cranor. 2013. What Matters to Users? Factors that Affect Users' Willingness to Share Information with Online Advertisers. In Proc. SOUPS. Google ScholarDigital Library
- Adam Lerner, Anna Kornfeld Simpson, Tadayoshi Kohno, and Franziska Roesner. 2016. Internet Jones and the Raiders of the Lost Trackers: An Archaeological Study of Web Tracking from 1996 to 2016. In Proc. USENIX Security.Google Scholar
- Aleksandar Matic, Martin Pielot, and Nuria Oliver. 2017. "OMG! How did it know that?": Reactions to Highly-Personalized Ads. In Proc. UMAP. Google ScholarDigital Library
- Jonathan R. Mayer and John C. Mitchell. 2012. Third-party web tracking: Policy and technology. In Proc. IEEE S&P. Google ScholarDigital Library
- Aleecia M. McDonald and Lorrie Faith Cranor. 2010. Americans' Attitudes About Internet Behavioral Advertising Practices. In Proc. WPES. Google ScholarDigital Library
- William Melicher, Mahmood Sharif, Joshua Tan, Lujo Bauer, Mihai Christodorescu, and Pedro Giovanni Leon. 2016. (Do Not) Track Me Sometimes: Users Contextual Preferences for Web Tracking. PoPETS 2 (2016), 135--154.Google Scholar
- Mozilla. Lightbeam. Retrieved September 2017 from http://www.mozilla.org/en-US/lightbeam/Google Scholar
- Oracle. Oracle Data Cloud Registry. Retrieved September 2017 from http://www.bluekai.com/registry/Google Scholar
- Javier Parra-Arnau, Jagdish Prasad Achara, and Claude Castelluccia. 2017. MyAdChoices: Bringing transparency and control to online advertising. ACM TWEB 11, 1 (2017). Google ScholarDigital Library
- Angelisa C. Plane, Elissa M. Redmiles, Michelle M. Mazurek, and Michael Carl Tschantz. 2017. Exploring User Perceptions of Discrimination in Online Targeted Advertising. In Proc. USENIX Security.Google Scholar
- Emilee Rader and Rebecca Gray. 2015. Understanding user beliefs about algorithmic curation in the Facebook news feed. In Proc. CHI. Google ScholarDigital Library
- Franziska Roesner, Tadayoshi Kohno, and David Wetherall. 2012. Detecting and Defending Against Third-party Tracking on the Web. In Proc. NSDI. Google ScholarDigital Library
- Sonam Samat, Alessandro Acquisti, and Linda Babcock. 2017. Raise the Curtains: The Effect of Awareness About Targeting on Consumer Attitudes and Purchase Intentions. In Proc. SOUPS.Google Scholar
- Florian Schaub, Aditya Marella, Pranshu Kalvani, Blase Ur, Chao Pan, Emily Forney, and Lorrie Faith Cranor. 2016. Watching Them Watching Me: Browser Extensions' Impact on User Privacy Awareness and Concern. In Proc. USEC.Google ScholarCross Ref
- Christopher A. Summers, Robert W. Smith, and Rebecca Walker Reczek. 2016. An Audience of One: Behaviorally Targeted Ads as Implied Social Labels. Journal of Consumer Research 43 (June 2016), 156--178.Google ScholarCross Ref
- Latanya Sweeney. 2013. Discrimination in online ad delivery. CACM 56, 5 (2013), 44--54. Google ScholarDigital Library
- Omer Tene and Jules Polonetsky. 2014. A Theory of Creepy: Technology, Privacy, and Shifting Social Norms. Yale Journal of Law and Technology 16, 1 (2014), 2.Google Scholar
- Florian Tramer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Jean-Pierre Hubaux, Mathias Humbert, Ari Juels, and Huang Lin. 2017. FairTest: Discovering unwarranted associations in data-driven applications. In Proc. EuroS&P.Google ScholarCross Ref
- Joseph Turow, Jennifer King, Chris Jay Hoofnagle, Amy Bleakley, and Michael Hennessy. 2009. Americans Reject Tailored Advertising and Three Activities that Enable It. SSRN (2009).Google Scholar
- Blase Ur, Pedro Giovanni Leon, Lorrie Faith Cranor, Richard Shay, and Yang Wang. 2012. Smart, useful, scary, creepy: Perceptions of online behavioral advertising. In Proc. SOUPS. Google ScholarDigital Library
- Jeffrey Warshaw, Nina Taft, and Allison Woodruff. 2016. Intuitions, Analytics, and Killing Ants: Inference Literacy of High School-educated Adults in the US. In Proc. SOUPS.Google Scholar
- Craig E. Wills and Can Tatar. 2012. Understanding what they do with what they know. In Proc. WPES. Google ScholarDigital Library
- Yaxing Yao, Davide Lo Re, and Yang Wang. 2017. Folk Models of Online Behavioral Advertising. In Proc. CCSW. Google ScholarDigital Library
- Zhonghao Yu, Sam Macbeth, Konark Modi, and Josep M. Pujol. 2016. Tracking the Trackers. In Proc. WWW. Google ScholarDigital Library
Index Terms
- Unpacking Perceptions of Data-Driven Inferences Underlying Online Targeting and Personalization
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