{"id":56,"date":"2022-07-28T09:24:01","date_gmt":"2022-07-28T09:24:01","guid":{"rendered":"https:\/\/metric.qcri.org\/blog\/?p=56"},"modified":"2022-07-28T09:24:02","modified_gmt":"2022-07-28T09:24:02","slug":"analyzing-advertising-labels-testing-consumers-recognition-of-paid-content-online","status":"publish","type":"post","link":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/analyzing-advertising-labels-testing-consumers-recognition-of-paid-content-online\/","title":{"rendered":"Analyzing Advertising Labels: Testing Consumers\u2019 Recognition of Paid Content Online"},"content":{"rendered":"\n<p>In 2014-2015, the U.S. Federal Trade Commission (FTC) commissioned a study to assess consumers\u2019 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.<\/p>\n\n\n\n<p>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\u2019 ability to recognize them.<\/p>\n\n\n\n<p>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.<\/p>\n\n\n\n<p>The participants\u2019 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\u2019 ability to recognize ads, paid content, and paid search results. We conclude with implications of the results and areas for future research.<\/p>\n\n\n\n<p>Johnson, J.,\u00a0Jansen, B. J., Hastak, M., and Raval, D. (2018)\u00a0<a rel=\"noreferrer noopener\" href=\"http:\/\/www.bernardjjansen.com\/uploads\/2\/4\/1\/8\/24188166\/lbw517.pdf\" target=\"_blank\">Analyzing Advertising Labels: Testing Consumers\u2019 Recognition of Paid Content Online<\/a>. ACM CHI Conference on Human Factors in Computing Systems (CHI2018) (Extended Abstract), Montr\u00e9al, Canada, 21-26 April, LBW517.\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In 2014-2015, the U.S. Federal Trade Commission (FTC) commissioned a study to assess consumers\u2019 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 &#8230; <a title=\"Analyzing Advertising Labels: Testing Consumers\u2019 Recognition of Paid Content Online\" class=\"read-more\" href=\"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/analyzing-advertising-labels-testing-consumers-recognition-of-paid-content-online\/\" aria-label=\"More on Analyzing Advertising Labels: Testing Consumers\u2019 Recognition of Paid Content Online\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-56","post","type-post","status-publish","format-standard","hentry","category-user-study"],"jetpack_featured_media_url":"","jetpack-related-posts":[{"id":20,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/how-does-personification-impact-ad-performance-and-empathy-an-experiment-with-online-advertising\/","url_meta":{"origin":56,"position":0},"title":"How Does Personification Impact Ad Performance and Empathy? An Experiment with Online Advertising","date":"July 28, 2022","format":false,"excerpt":"This research explores the value of personas for supporting professional advertisers to design adverts for social media. We test if a personified user group (PUG), when provided to online ad designers, results in better ad performance than when using a non-personified user group (NUG) that had no face picture or\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":206,"url":"https:\/\/metric.qcri.org\/blog\/2024\/06\/12\/integrated-marketing-courses-with-metric\/","url_meta":{"origin":56,"position":1},"title":"Integrated Marketing Courses with METRIC","date":"June 12, 2024","format":false,"excerpt":"Did you know the global advertising industry is worth over 500 billion US dollars as of 2023? However, not all ads are successful. 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Are you looking to improve your research and teaching methods? METRIC can help you and your students engage with user studies at scale.Are you looking to improve your research and teaching methods? METRIC can help you and your students engage with user studies\u2026","rel":"","context":"In &quot;analytics&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2024\/07\/METRIC_Teaching_July_2024.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":30,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/using-the-taxonomy-of-cognitive-learning-to-model-online-searching\/","url_meta":{"origin":56,"position":3},"title":"Using the taxonomy of cognitive learning to model online searching","date":"July 28, 2022","format":false,"excerpt":"In this research, we investigated whether a learning process has unique information searching characteristics. The results of this research show that information searching is a learning process with unique searching characteristics specific to particular learning levels. In a laboratory experiment, we studied the searching characteristics of 72 participants engaged in\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":60,"url":"https:\/\/metric.qcri.org\/blog\/2022\/07\/28\/findings-of-a-user-study-of-automatically-generated-personas\/","url_meta":{"origin":56,"position":4},"title":"Findings of a User Study of Automatically Generated Personas","date":"July 28, 2022","format":false,"excerpt":"We report findings and implications from a seminaturalistic user study of a system for Automatic Persona Generation (APG) using large-scale audience data of an organization\u2019s social media channels conducted at the workplace of a major international corporation. Thirteen participants from a range of positions within the company engaged with the\u2026","rel":"","context":"In &quot;user study&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":155,"url":"https:\/\/metric.qcri.org\/blog\/2024\/01\/16\/metric-with-react-js-elevating-user-engagement-analytics\/","url_meta":{"origin":56,"position":5},"title":"METRIC with React JS: Elevating User Engagement Analytics","date":"January 16, 2024","format":false,"excerpt":"In the ever-evolving landscape of web development, understanding user engagement is crucial for crafting seamless and user-friendly experiences. To aid developers in this quest, METRIC, a powerful tool designed for collecting, measuring, analyzing, and reporting user engagement, has now extended its capabilities to seamlessly integrate with websites developed using React.\u2026","rel":"","context":"In &quot;analytics&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/metric.qcri.org\/blog\/wp-content\/uploads\/2024\/01\/how-to-learn-react-js-quickly-4.jpeg?fit=1200%2C800&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/comments?post=56"}],"version-history":[{"count":1,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/56\/revisions"}],"predecessor-version":[{"id":57,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/posts\/56\/revisions\/57"}],"wp:attachment":[{"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/media?parent=56"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/categories?post=56"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/metric.qcri.org\/blog\/wp-json\/wp\/v2\/tags?post=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}