Detecting Adversarial Advertisements in the Wild
Venue
Proceedings of the 17th ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, KDD (2011)
Publication Year
2011
Authors
D. Sculley, Matthew Eric Otey, Michael Pohl, Bridget Spitznagel, John Hainsworth, Yunkai Zhou
BibTeX
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.
