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The Value of First Impressions

The Impact of Ad-Blocking on Web QoE
  • James NewmanEmail author
  • Fabián E. Bustamante
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11419)

Abstract

We present the first detailed analysis of ad-blocking’s impact on user Web quality of experience (QoE). We use the most popular web-based ad-blocker to capture the impact of ad-blocking on QoE for the top Alexa 5,000 websites. We find that ad-blocking reduces the number of objects loaded by 15% in the median case, and that this reduction translates into a 12.5% improvement on page load time (PLT) and a slight worsening of time to first paint (TTFP) of 6.54%. We show the complex relationship between ad-blocking and quality of experience - despite the clear improvements to PLT in the average case, for the bottom 10 percentile, this improvement comes at the cost of a slowdown on the initial responsiveness of websites, with a 19% increase to TTFP. To understand the relative importance of this trade-off on user experience, we run a large, crowd-sourced experiment with 1,000 users in Amazon Turk. For this experiment, users were presented with websites for which ad-blocking results in both, a reduction of PLT and a significant increase in TTFP. We find, surprisingly, 71.5% of the time users show a clear preference for faster first paint over faster page load times, hinting at the importance of first impressions on web QoE.

References

  1. 1.
    Google Cloud Computing, Hosting Services & APIs—Google cloud. https://cloud.google.com/
  2. 2.
    Alexa: The top 500 Sites on the Web (2018). https://www.alexa.com/topsites
  3. 3.
    Barrett, C.: Filter Lists (2018). https://filterlists.com/
  4. 4.
    Brave Software Inc.: Brave Browser: Secure, Fast and Private Web Browser with Adblocker. https://brave.com/
  5. 5.
    Butkiewicz, M., Madhyastha, H.V., Sekar, V.: Measurements, metrics and implications. In: Proceedings of IMC, Understanding Website Complexity (2011)Google Scholar
  6. 6.
    Butkiewicz, M., Wang, D., Wu, Z., Madhyastha, H.V., Sekar, V.: KLOTSKI: reprioritizing web content to improve user experience on mobile devices. In: Proceedings of USENIX NSDI (2015)Google Scholar
  7. 7.
    Chrome: Chrome Webrequest API. https://developer.chrome.com/extensions/webRequest. Accessed 25 May 2018
  8. 8.
    Cortland, M.: 2017 Adblock Report (2017). https://pagefair.com/blog/2017/adblockreport/
  9. 9.
    Disconnect: Disconnect.me. https://disconnect.me/
  10. 10.
    Dong, X., Tran, M., Liang, Z., Jiang, X.: Adsentry: comprehensive and flexible confinement of Javascript-based advertisements. In: Proceedings of ACSAC (2011)Google Scholar
  11. 11.
    Fanboys: Fanboys enhanced tracking list. https://fanboy.co.nz/filters.html. Accessed 24 May 2018
  12. 12.
    Python Software Foundation haralyzer 1.4.11 (2017). https://pypi.org/project/haralyzer/
  13. 13.
    Google: Chrome Canary. https://www.google.com/chrome/browser/canary.html. Accessed 25 May 2018
  14. 14.
    Jimdo: Blockzilla: Ad Blocking List (2018). https://blockzilla.jimdo.com/
  15. 15.
    Kelton, C., Ryoo, J., Balasubramanian, A., Das, S.R.: Improving user perceived page load times using gaze. In: Proceedings of USENIX NSDI (2017)Google Scholar
  16. 16.
    Krammer, V.: An effective defense against intrusive web advertising. In: Proceedings of Conference on Privacy, Security and Trust (2008)Google Scholar
  17. 17.
    Langley, A., et al.: Design and internet-scale deployment. In: Proceedings of ACM SIGCOMM, The QUIC Transport Protocol (2017)Google Scholar
  18. 18.
    Lifehacker: Ad Blocking. https://lifehacker.com/tag/ad-blocking. Accessed 25 May 2018
  19. 19.
    Malloy, M., Matthew, M., Cahn, A., Barford, P.: Ad blockers: global prevalence and impact. In: Proceedings of IMC (2016)Google Scholar
  20. 20.
    Meenan, P.: WebPageTest (2018). http://www.webpagetest.org/. Accessed 24 May 2018
  21. 21.
    Metwalley, H., Traverso, S., Mellia, M., Miskovic, S., Baldi, M.: The online tracking horde: a view from passive measurements. In: Proceedings of TMA (2015)Google Scholar
  22. 22.
    Netravali, R., Goyal, A., Mickens, J., Balakrishnan, H.: Polaris: faster page loads using fine-grained dependency tracking. In: Proceedings of USENIX NSDI (2016)Google Scholar
  23. 23.
    Netravali, R., et al.: Mahimahi: accurate record-and-replay for HTTPGoogle Scholar
  24. 24.
    Poss, T.: How Does Load Speed Affect Conversion Rate? https://blogs.oracle.com/marketingcloud/how-does-load-speed-affect-conversion-rate. Accessed 14 Jan 2016
  25. 25.
    Post, E.L., Sekharan, C.N.: Comparative study and evaluation of online ad-blockers. In: Proceedings of International Conference on Information Science and Security (2015)Google Scholar
  26. 26.
    Pujol, E., Hohlfeld, O., Feldmann, A.: Annoyed users: ads and ad-block usage in the wild. In: Proceedings of IMC (2015)Google Scholar
  27. 27.
    Ruamviboonsuk, V., Netravali, R., Uluyol, M., Madhyastha, H.V.: Vroom: accelerating the mobile web with server-aided dependency resolution. In: Proceedings of ACM SIGCOMM (2017)Google Scholar
  28. 28.
    Sivakumar, A., Narayanan, S.P., Gopalakrishnan, V., Lee, S., Rao, S., Sen, S.: PARCEL: proxy assisted browsing in cellular networks for energy and latency reduction. In: Proceedings of ACM CoNEXT (2014)Google Scholar
  29. 29.
    Sood, A.K., Enbody, R.J.: Malvertising - exploiting web advertising. Comput. Fraud Secur. 2011(4), 11–16 (2011)CrossRefGoogle Scholar
  30. 30.
    Sundaresan, S., Magharei, N., Feamster, N., Teixeira, R., Crawford, S.: Web performance bottlenecks in broadband access networks. In: Proceedings of SIGMETRICS (2013)Google Scholar
  31. 31.
    The Chromium Projects: SPDY: An Experimental Protocol for a Faster Web (2012). https://www.chromium.org/spdy/spdy-whitepaper
  32. 32.
    The EasyList Authors: Easylist (2018). https://easylist.to/
  33. 33.
    Varvello, M., Blackburn, J., Naylor, D., Papagiannaki, K.: Eyeorg: a platform for crowdsourcing web quality of experience measurements. In: Proceedings of ACM CoNEXT (2016)Google Scholar
  34. 34.
    Walls, R., Kilmer, E., Lageman, N., McDaniel, P.D.: Measuring the impact and perception of acceptable advertisements. In: Proceedings of IMC (2015)Google Scholar
  35. 35.
  36. 36.
    Wang, X.S., Balasubramanian, A., Wetherall, D.: Speeding Up Web Page Loads with Shandian. In: Proceedings of USENIX NSDI (2016)Google Scholar
  37. 37.
    Wills, C.E., Uzunoglu, D.C.: What ad blockers are (and are not) doing. In: Fourth IEEE Workshop on Hot Topics in Web Systems and Technologies (2016)Google Scholar
  38. 38.
    Wired: The New Chrome and Safari Will Reshape the Web (2017). https://www.wired.com/2017/06/new-chrome-safari-will-reshape-web/
  39. 39.
    Work, S.: How Loading Time Affects Your Bottom Line, 28 April 2011. https://blog.kissmetrics.com/loading-time/. Accessed 22 May 2018
  40. 40.
    Zarras, A., Kapravelos, A., Stringhini, G., Holz, T., Kruegel, C., Vigna, G.: The dark alleys of Madison avenue: understanding malicious advertisements. In: Proceedings of IMC (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Northwestern UniversityEvanstonUSA

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