document: Detecting adversarial advertisements in the wild 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O