2011 DetectingAdversarialAdvertiseme

• (Sculley et al., 2011) ⇒ D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, and Yunkai Zhou. (2011). “Detecting Adversarial Advertisements in the Wild.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020455

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Detecting+Adversarial+Advertisements+in+the+Wild
• http://dl.acm.org/citation.cfm?id=2020408.2020455&preflayout=flat#citedby

Quotes

Author Keywords

• Adversarial learning; applications; data mining; experimentation; online advertisement

Abstract

In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 DetectingAdversarialAdvertiseme	D. Sculley Matthew Eric Otey Michael Pohl Bridget Spitznagel John Hainsworth Yunkai Zhou			Detecting Adversarial Advertisements in the Wild				10.1145/2020408.2020455		2011