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Challenges in measuring online advertising systems

Published: 01 November 2010 Publication History

Abstract

Online advertising supports many Internet services, such as search, email, and social networks. At the same time, there are widespread concerns about the privacy loss associated with user targeting. Yet, very little is publicly known about how ad networks operate, especially with regard to how they use user information to target users. This paper takes a first principled look at measurement methodologies for ad networks. It proposes new metrics that are robust to the high levels of noise inherent in ad distribution, identifies measurement pitfalls and artifacts, and provides mitigation strategies. It also presents an analysis of how three different classes of advertising -- search, contextual, and social networks, use user profile information today.

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Cited By

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  • (2023)Understanding the Privacy Risks of Popular Search Engine Advertising SystemsProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624823(370-382)Online publication date: 24-Oct-2023
  • (2023)Collaborative Ad Transparency: Promises and Limitations2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179448(2639-2657)Online publication date: May-2023
  • (2023)Tobacco advertising exposure and product use among young adults: An ecological momentary assessment approachAddictive Behaviors10.1016/j.addbeh.2022.107601139(107601)Online publication date: Apr-2023
  • Show More Cited By

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cover image ACM Conferences
IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
November 2010
496 pages
ISBN:9781450304832
DOI:10.1145/1879141
  • Program Chair:
  • Mark Allman
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]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2010

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Author Tags

  1. advertising
  2. behavioral targeting
  3. churn
  4. contextual
  5. facebook
  6. google
  7. privacy
  8. similarity

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IMC '10
IMC '10: Internet Measurement Conference
November 1 - 30, 2010
Melbourne, Australia

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Overall Acceptance Rate 277 of 1,083 submissions, 26%

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Cited By

View all
  • (2023)Understanding the Privacy Risks of Popular Search Engine Advertising SystemsProceedings of the 2023 ACM on Internet Measurement Conference10.1145/3618257.3624823(370-382)Online publication date: 24-Oct-2023
  • (2023)Collaborative Ad Transparency: Promises and Limitations2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179448(2639-2657)Online publication date: May-2023
  • (2023)Tobacco advertising exposure and product use among young adults: An ecological momentary assessment approachAddictive Behaviors10.1016/j.addbeh.2022.107601139(107601)Online publication date: Apr-2023
  • (2022)Who Knows I Like Jelly Beans? An Investigation Into Search PrivacyProceedings on Privacy Enhancing Technologies10.2478/popets-2022-00532022:2(426-446)Online publication date: 3-Mar-2022
  • (2022)The Concentration-after-Personalisation Index (CAPI): Governing effects of personalisation using the example of targeted online advertisingBig Data & Society10.1177/205395172211325359:2Online publication date: 5-Dec-2022
  • (2022)Analyzing the Impact and Accuracy of Facebook Activity on Facebook's Ad-Interest Inference ProcessProceedings of the ACM on Human-Computer Interaction10.1145/35129236:CSCW1(1-34)Online publication date: 7-Apr-2022
  • (2022)Privacy in targeted advertising on mobile devices: a surveyInternational Journal of Information Security10.1007/s10207-022-00655-x22:3(647-678)Online publication date: 24-Dec-2022
  • (2022)Machine Learning and Deep Learning Techniques for Phishing Threats and ChallengesCyber‐Physical Systems10.1002/9781119836636.ch6(123-146)Online publication date: 27-Jul-2022
  • (2021)CHASEProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481902(4352-4361)Online publication date: 26-Oct-2021
  • (2021)Utilizing Web Trackers for Sybil DefenseACM Transactions on the Web10.1145/345044415:2(1-19)Online publication date: 22-Apr-2021
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