skip to main content
10.1145/2987443.2987460acmconferencesArticle/Chapter ViewAbstractPublication PagesimcConference Proceedingsconference-collections
short-paper

Ad Blockers: Global Prevalence and Impact

Published:14 November 2016Publication History

ABSTRACT

Ad blockers are a formidable threat to the vitality of the online advertising eco-system. Understanding their prevalence and impact is challenging due to the massive scale and diversity of the eco-system. In this paper, we utilize unique data gathering assets to assess the prevalence and impact of ad blockers from an Internet-wide perspective. Our study is based on (i) a 2 million person world-wide user panel that provides ground truth for ad blocker installations and (ii) telemetry from large number of publisher web pages and ads served to publishers. We describe a novel method for assessing the prevalence of ad blocker installations that is based on Mixture Proportion Estimation. We apply this method to nearly 2 trillion web transactions collected over the period of 1 month (February 2016), to derive ad blocker prevalence estimates for desktop systems in diverse geographic areas and for diverse demographic groups. Next, using deployment estimates we consider the impact of ad blockers on users and on publisher sites. Specifically, we report on the reduction of ads shown to users with ad blockers installed and show that even though a user may have an ad blocker installed, they are still exposed to a significant number of ads. We also characterize the impact of ad blockers across different categories of publisher sites including those that may be participating in whitelisting.

References

  1. A. Agresti and M. Kateri. Categorical Data Analysis. Springer, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  2. P. Barford, I. Canadi, D. Krushevskaja, Q. Ma, and S. Muthukrishnan. Adscape: Harvesting and Analyzing Online Display Ads. In Proceedings of the 23rd World Wide Web Conference, Seoul, Korea, April 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Blanchfield. PageFair, 2016. https://pagefair.com.Google ScholarGoogle Scholar
  4. S. Nath. MAdScope: Characterizing Mobile In-App Targeted Ads. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, Florence, Italy, May 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. W. Palant. Adblock plus, 2016. http://adblockplus.org.Google ScholarGoogle Scholar
  6. E. Post and C. Sekharan. Comparative Study and Evaluation of Online Ad-Blockers. In Proceedings of the 2nd International Conference on Information Science and Security, Seoul, Korea, December 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. E. Pujol, O. Hohlfeld, and A. Feldmann. Annoyed Users: Ads and Ad-Block Usage in the Wild. In Proceedings of the ACM Internet Measurement Conference, Tokyo, Japan, October 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. Scott. A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, San Diego, CA, May 2015.Google ScholarGoogle Scholar
  9. R. Walls, E. Kilmer, N. Lageman, and P. McDaniel. Measuring the Impact and Perception of Acceptable Advertisements. In Proceedings of the ACM Internet Measurement Conference, Tokyo, Japan, October 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Ad Blockers: Global Prevalence and Impact

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            IMC '16: Proceedings of the 2016 Internet Measurement Conference
            November 2016
            570 pages
            ISBN:9781450345262
            DOI:10.1145/2987443

            Copyright © 2016 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]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 14 November 2016

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • short-paper

            Acceptance Rates

            IMC '16 Paper Acceptance Rate48of184submissions,26%Overall Acceptance Rate277of1,083submissions,26%

            Upcoming Conference

            IMC '24
            ACM Internet Measurement Conference
            November 4 - 6, 2024
            Madrid , AA , Spain

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader