research-article

What do online behavioral advertising privacy disclosures communicate to users?

Authors:

Pedro Giovanni Leon,

Justin Cranshaw,

Lorrie Faith Cranor,

Guzi XuAuthors Info & Claims

WPES '12: Proceedings of the 2012 ACM workshop on Privacy in the electronic society

Pages 19 - 30

https://doi.org/10.1145/2381966.2381970

Published: 15 October 2012 Publication History

Abstract

Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices", the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We dicuss challenges in crafting disclosures and provide suggestions for improvement.

References

[1]

J. C. Andrews, S. Burton, and J. Kees. Is simpler always better? Consumer evaluations of front-of-package nutrition symbols. Journal of Public Policy & Marketing, 30(2):175--190, 2011.

[2]

A. Antón, J. Earp, Q. He, W. Stufflebeam, D. Bolchini, and C. Jensen. The lack of clarity in financial privacy policies and the need for standardization. IEEE Security & Privacy, 2(2):36--45, 2004.

Digital Library

[3]

H. Beales. The value of behavioral targeting. Technical report, The Netwok Advertising Initiative, 2010.

[4]

D. M. Boush. How advertising slogans can prime evaluations of brand extensions. Psychology and Marketing, 10(1):67--78, 1993.

[5]

M. Buhrmester, T. Kwang, and S. D. Gosling. Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1):3--5, 2011.

[6]

L. F. Cranor, P. Guduru, and M. Arjula. User interfaces for privacy agents. ACM Transactions on Computer-Human Interaction, 13, 2006.

Digital Library

[7]

M. Dahlén and S. Rosengren. Brands affect slogans affect brands? Competitive interference, brand equity and the brand-slogan link. Journal of Brand Management, 12(3):151--164, 2005.

[8]

Digital Advertising Alliance. Self-Regulatory Principles for Online Behavioral Advertising, July 2009.

[9]

Digital Advertising Alliance. Advertising option icon application, 2012.

[10]

Federal Trade Commission. Self-regulatory principles for online behavioral advertising. 2009.

[11]

L. Garrison, M. Hastak, J. Hogarth, S. Kleimann, and L. Levy. Designing evidence-based disclosures: A case study of financial privacy notices. Journal of Consumer Affairs. 2012.

[12]

K. Haramundanis. Why icons cannot stand alone. SIGDOC Asterisk Journal of Computer Documentation, 20(2):1--8, May 1996.

Digital Library

[13]

M. Hastak and M. J. Culnan. Online behavioral advertising "icon" study. www.futureofprivacy.org/final_report.pdf, 2010.

[14]

S.-C. Huang and R. G. Bias. Icons versus texts in accuracy and efficiency. In Proc. iConference, 2012.

[15]

P. G. Ipeirotis. Demographics of Mechanical Turk. Technical Report CeDER-10-01, New York University, 2010.

[16]

C. Jensen and C. Potts. Privacy policies as decision-making tools: an evaluation of online privacy notices. In Proc. CHI, 2004.

Digital Library

[17]

P. G. Kelley, L. Cesca, J. Bresee, and L. F. Cranor. Standardizing privacy notices: An online study of the nutrition label approach. In Proc. CHI, 2010.

Digital Library

[18]

S. Komanduri, R. Shay, G. Norcie, B. Ur, and L. F. Cranor. AdChoices? Compliance with online behavioral advertising notice and choice requirements. I/S: A Journal of Law and Policy for the Information Society, 7(3):603--638, 2011.

[19]

B. Krishnamurthy and C. Wills. Privacy diffusion on the web: a longitudinal perspective. In Proc. WWW, 2009.

Digital Library

[20]

M. L. A. Kunnath, R. A. Cornell, M. K. Kysilka, and L. Witta. An experimental research study on the effect of pictorial icons on a user-learner's performance. Computers in Human Behavior, 23(3):1454--1480, May 2007.

Digital Library

[21]

A. Y. Lee. Effects of implicit memory on memory-based versus stimulus-based brand choice. Journal of Marketing Research, 39(4):pp. 440--454, 2002.

[22]

A. M. McDonald and L. F. Cranor. The cost of reading privacy policies. I/S: A Journal of Law and Policy for the Information Society, 4(3):543--568, 2008.

[23]

A. M. McDonald and L. F. Cranor. Beliefs and behaviors: Internet users' understanding of behavioral advertising. In Proc. TPRC, 2010.

[24]

A. M. Mcdonald, R. W. Reeder, P. G. Kelley, and L. F. Cranor. A comparative study of online privacy policies and formats. In Proc. PETS, 2009.

Digital Library

[25]

Microsoft. Privacy in Internet Explorer 6, June 2011.

[26]

G. Paolacci, J. Chandler, and P. G. Ipeirotis. Running experiments on Amazon Mechanical Turk. Judgment and Decision Making, 5(5), 2010.

[27]

K. Purcell, J. Brenner, and L. Rainie. Search engine use 2012. Technical report, March 2012.

[28]

J. Ross, L. Irani, M. S. Silberman, A. Zaldivar, and B. Tomlinson. Who are the crowdworkers? Shifting demographics in Mechanical Turk. In Proc. CHI Extended Abstracts, 2010.

Digital Library

[29]

J. Turow, J. King, C. J. Hoofnagle, A. Bleakley, and M. Hennessy. Americans reject tailored advertising and three activities that enable it. SSRN eLibrary, 2009.

[30]

B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang. Smart, Useful, Scary, Creepy: Perceptions of online behavioral advertising. In Proc. SOUPS, 2012.

Digital Library

[31]

A. Wang. Visual priming of pharmaceutical advertising disclosures: effects of a motivation factor. Corporate Communications: An International Journal, 17(1), 2012.

[32]

S. Wiedenbeck. The use of icons and labels in an end user application program: an empirical study of learning and retention. Behaviour & Information Technology, 18(2), 1999.

[33]

S. S. Williams and C. P. Koepke. Using multiple methods to identify theme lines for media marketing campaigns to promote Medicare information sources. Journal of Consumer Marketing, 23(6), 2006.

[34]

J. Yan, N. Liu, G. Wang, W. Zhang, Y. Jiang, and Z. Chen. How much can behavioral targeting help online advertising? In Proc. WWW, 2009.

Digital Library

Cited By

Lee HLogas JYang SLi ZBarbosa NWang YDas S(2023)When and Why Do People Want Ad Targeting Explanations? Evidence from a Four-Week, Mixed-Methods Field Study2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179452(2903-2920)Online publication date: May-2023
https://doi.org/10.1109/SP46215.2023.10179452
Zhang WRodgers S(2023)Linking Ethnicity Targeting with Artificial Intelligence and Data Collection: Perceptions and Behavioral Responses of Black ConsumersJournal of Current Issues & Research in Advertising10.1080/10641734.2023.221202244:3(373-391)Online publication date: 19-Jul-2023
https://doi.org/10.1080/10641734.2023.2212022
Wasay ATatbul NGottschlich J(2022)Machine programmingProceedings of the VLDB Endowment10.14778/3554821.355489215:12(3754-3757)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554892
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Index Terms

What do online behavioral advertising privacy disclosures communicate to users?
1. Human-centered computing
2. Social and professional topics
  1. Computing / technology policy

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cover image ACM Conferences

WPES '12: Proceedings of the 2012 ACM workshop on Privacy in the electronic society

October 2012

150 pages

ISBN:9781450316637

DOI:10.1145/2381966

General Chair:
Ting Yu
North Carolina State University, USA
,
Program Chair:
Nikita Borisov
University of Illinois at Urbana-Champaign, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Publication History

Published: 15 October 2012

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CCS'12: the ACM Conference on Computer and Communications Security

October 15, 2012

North Carolina, Raleigh, USA

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Overall Acceptance Rate 106 of 355 submissions, 30%

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Cited By

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https://doi.org/10.1109/SP46215.2023.10179452
Zhang WRodgers S(2023)Linking Ethnicity Targeting with Artificial Intelligence and Data Collection: Perceptions and Behavioral Responses of Black ConsumersJournal of Current Issues & Research in Advertising10.1080/10641734.2023.221202244:3(373-391)Online publication date: 19-Jul-2023
https://doi.org/10.1080/10641734.2023.2212022
Wasay ATatbul NGottschlich J(2022)Machine programmingProceedings of the VLDB Endowment10.14778/3554821.355489215:12(3754-3757)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.14778/3554821.3554892
Badaro GPapotti P(2022)Transformers for tabular data representationProceedings of the VLDB Endowment10.14778/3554821.355489015:12(3746-3749)Online publication date: 1-Aug-2022
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