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An Evaluation of Statistical Approaches to Text Categorization
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Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
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Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Improving spam detection based on structural similarity
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MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Various services on the Web 2.0 offer functions that allow users to post videos as response to a discussion topic. As an example, YouTube allows users to post video responses to an opening video topic. Such a video response can be a polluted video, aiming at increasing the popularity of the discussed topic, disseminating advertisements, distributing pornography or simple degrading the system reputation. Content pollution may compromise user satisfaction with the system since users cannot easily identify polluted content before watching at least a segment of it, consuming system resources, especially bandwidth. This work approaches the problem of detecting the malicious users who post polluted content. To do it, we construct a test collection with users from YouTube. Using attributes of users and videos, we apply a classification algorithm as approach to detect owners of polluted content. Additionally, we build a simulator to verify the applicability of our approach in different scenarios.