An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Understanding video interactions in youtube
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Online video social networks provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users- promoters- may try to gain visibility to a specific video by posting a large number of responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. In this paper, we address the issue of detecting video spammers and promoters. Towards that end, we build a test collection of real YouTube users. Using our test collection we investigate the feasibility of using a supervised classification algorithm to detect spammers and promoters. We found that our approach is able to correctly identify the majority of the promoters, misclassifying only a small percentage of legitimate users. In contrast, although we are able to detect a significant fraction of spammers, they showed to be much harder to distinguish from legitimate users.