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Analyzing Advertising Labels: Testing Consumers' Recognition of Paid Content Online

Published: 20 April 2018 Publication History

Abstract

In 2014-2015, the U.S. Federal Trade Commission (FTC) commissioned a study to assess consumers' ability to recognize ads and other paid content in online search results and news/article feeds. The co-authors designed the study, oversaw its execution, and analyzed the results, with support from FTC staff. The goals of the research were to assess the effectiveness of methods that online services use to label ads, and to see if alternative methods of labeling ads could improve consumers' ability to recognize them. In a controlled experiment, 48 consumers interacted with both desktop and mobile Web pages that were captured from search and online magazine websites. In half of the conditions, the Web pages were modified based on established Web design guidelines to improve the clarity of ad labeling. The participants' behavior, comments, and eye movements were recorded. Initial findings of this experiment are: (a) consumers cannot always distinguish ads, paid content, and paid search results from unpaid content, and (b) improving the salience and placement of labels based on established UI design guidelines can improve consumers' ability to recognize ads, paid content, and paid search results. We conclude with implications of the results and areas for future research.

References

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Arch, A. (2008) "Web Accessibility for Older Users: A Literature Review", WorldWideWeb Consortium (W3C).
[2]
FTC (2016), FTC Enforcement Policy Statement on Deceptively Formatted Advertisements, 81 Fed. Reg. 22596 (Apr 18).
[3]
FTC (2017), Press Release: FTC Publishes Staff Report on Exploratory Research Examining Consumer Recognition of Paid Search and Native Advertising Online (Dec 15).
[4]
FTC (2017), Blurred Lines: An Exploration of Consumers' Advertising Recognition in the Contexts of Search Engines and Native Advertising, (Dec 15).
[5]
Jansen, B. J. and Spink, A. (2009) Investigating Customer Click-through Behavior with Integrated Sponsored and Non-Sponsored Results, Int'l J. of Internet Marketing and Advertisement, 5(1/2), 74--94.
[6]
Johnson J. (2014) Designing with the Mind in Mind, 2nd Ed., Morgan Kaufman Publishers.
[7]
Navdeep S. Sahni, and Harikesh S. Nair (2017), Native Advertising, Sponsorship Disclosure and Consumer Deception: Evidence from Mobile Search-Ad Experiments, (Feb 23), SSRN.
[8]
Pernice, K., Estes, J. and Nielsen, J. (2013) Senior Citizens on the Web, Nielsen Norman Group Report.
[9]
Usability.gov, (2015) Research-Based Web Design and Usability Guidelines.
[10]
Wojdynski, B. (2017), The Deceptiveness of Sponsored News Articles: How Readers Recognize and Perceive Native Advertising, American Behavioral Scientist, 60(12), 1475--1491.

Cited By

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  • (2023)Examining accuracy-prompt efficacy in combination with using colored borders to differentiate news and social content onlineHarvard Kennedy School Misinformation Review10.37016/mr-2020-113Online publication date: 27-Feb-2023
  • (2023)Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted AdvertisingProceedings of the ACM on Human-Computer Interaction10.1145/36102097:CSCW2(1-37)Online publication date: 4-Oct-2023
  • (2023)Political advertisement on Facebook and Instagram in the run up to 2022 Italian general electionProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583598(13-22)Online publication date: 30-Apr-2023
  • Show More Cited By

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    cover image ACM Conferences
    CHI EA '18: Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
    April 2018
    3155 pages
    ISBN:9781450356213
    DOI:10.1145/3170427
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 20 April 2018

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    Author Tags

    1. ads
    2. advertising
    3. chi policy
    4. consumer
    5. deceptive ads
    6. ftc
    7. graphic design
    8. native ads
    9. regulation
    10. search engines
    11. search results
    12. user interface design

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    CHI EA '18 Paper Acceptance Rate 1,208 of 3,955 submissions, 31%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    Cited By

    View all
    • (2023)Examining accuracy-prompt efficacy in combination with using colored borders to differentiate news and social content onlineHarvard Kennedy School Misinformation Review10.37016/mr-2020-113Online publication date: 27-Feb-2023
    • (2023)Sociotechnical Audits: Broadening the Algorithm Auditing Lens to Investigate Targeted AdvertisingProceedings of the ACM on Human-Computer Interaction10.1145/36102097:CSCW2(1-37)Online publication date: 4-Oct-2023
    • (2023)Political advertisement on Facebook and Instagram in the run up to 2022 Italian general electionProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583598(13-22)Online publication date: 30-Apr-2023
    • (2021)Clandestino or Rifugiato?Anti-immigration Facebook Ad Targeting in Italy✱Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445082(1-15)Online publication date: 6-May-2021

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