Analyzing advertising labels: Testing consumers’ recognition of paid content online

Jeff Johnson, Bernard James Jansen, Manoj Hastak, Devesh Raval

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish (US)
Title of host publicationCHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationEngage with CHI
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450356206, 9781450356213
DOIs
StatePublished - Apr 20 2018
Event2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018 - Montreal, Canada
Duration: Apr 21 2018Apr 26 2018

Publication series

NameConference on Human Factors in Computing Systems - Proceedings
Volume2018-April

Other

Other2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018
CountryCanada
CityMontreal
Period4/21/184/26/18

Fingerprint

Labels
Websites
Marketing
Labeling
Testing
Eye movements
Experiments
Web Design

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Cite this

Johnson, J., Jansen, B. J., Hastak, M., & Raval, D. (2018). Analyzing advertising labels: Testing consumers’ recognition of paid content online. In CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI [LBW517] (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2018-April). Association for Computing Machinery. https://doi.org/10.1145/3170427.3188533
Johnson, Jeff ; Jansen, Bernard James ; Hastak, Manoj ; Raval, Devesh. / Analyzing advertising labels : Testing consumers’ recognition of paid content online. CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery, 2018. (Conference on Human Factors in Computing Systems - Proceedings).
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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.",
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Johnson, J, Jansen, BJ, Hastak, M & Raval, D 2018, Analyzing advertising labels: Testing consumers’ recognition of paid content online. in CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI., LBW517, Conference on Human Factors in Computing Systems - Proceedings, vol. 2018-April, Association for Computing Machinery, 2018 CHI Conference on Human Factors in Computing Systems, CHI EA 2018, Montreal, Canada, 4/21/18. https://doi.org/10.1145/3170427.3188533

Analyzing advertising labels : Testing consumers’ recognition of paid content online. / Johnson, Jeff; Jansen, Bernard James; Hastak, Manoj; Raval, Devesh.

CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery, 2018. LBW517 (Conference on Human Factors in Computing Systems - Proceedings; Vol. 2018-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Johnson J, Jansen BJ, Hastak M, Raval D. Analyzing advertising labels: Testing consumers’ recognition of paid content online. In CHI 2018 - Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems: Engage with CHI. Association for Computing Machinery. 2018. LBW517. (Conference on Human Factors in Computing Systems - Proceedings). https://doi.org/10.1145/3170427.3188533