Detecting Spammers and Content Promoters in Online Video Social Networks

Fabrício Benevenuto, Tiago Rodrigues, Virgílio A. F. Almeida, J. Almeida, Marcos André Gonçalves
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引用次数: 246

Abstract

A number of online video social networks, out of which YouTube is the most popular, 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, or simply pollution, 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 (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we go a step further by addressing the issue of detecting video spammers and promoters. Towards that end, we manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimates. Using our test collection, we provide a characterization of social and content attributes that may help distinguish each user class. We also investigate the feasibility of using a state-of-the-art supervised classification algorithm to detect spammers and promoters, and assess its effectiveness in our test collection. 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.
在线视频社交网络检测垃圾邮件发送者和内容推广者
许多在线视频社交网络,其中最受欢迎的是YouTube,提供了允许用户发布视频作为对讨论主题的回应的功能。这些特性为用户提供了将受污染的内容或仅仅是污染的内容引入系统的机会。例如,垃圾邮件发送者可能会发布一个不相关的视频作为对热门视频的回应,目的是增加更多用户观看该回应的可能性。此外,投机取势的用户(游戏邦注:即推广者)可能会试图通过发布大量(可能不相关的)回复来提高被回复视频的排名,使其出现在系统维护的顶级列表中,从而获得特定视频的可见性。内容污染可能会危及用户对该系统的信任,从而影响其促进社会互动的成功。尽管如此,现有的文献在提供对这个问题的深刻理解方面非常有限。在本文中,我们进一步解决了检测视频垃圾邮件发送者和推广者的问题。为此,我们手动构建了一个真实YouTube用户的测试集合,将他们分类为垃圾邮件发送者、推广者和合法用户。使用我们的测试集合,我们提供了社会属性和内容属性的特征,可以帮助区分每个用户类别。我们还研究了使用最先进的监督分类算法来检测垃圾邮件发送者和推广者的可行性,并评估了其在我们的测试集合中的有效性。我们发现,我们的方法能够正确识别大多数推广者,只有一小部分合法用户被错误分类。相比之下,尽管我们能够检测到相当一部分垃圾邮件发送者,但要将他们与合法用户区分开来要困难得多。
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