Identifying video spammers in online social networks

In many video social networks, including YouTube, users are permitted to post video responses to other users' videos. Such a response can be legitimate or can be a video response spam, which is a video response whose content is not related to the topic being discussed. Malicious users may post video response spam for several reasons, including increase the popularity of a video, marketing advertisements, distribute pornography, or simply pollute the system. In this paper we consider the problem of detecting video spammers. We first construct a large test collection of YouTube users, and manually classify them as either legitimate users or spammers. We then devise a number of attributes of video users and their social behavior which could potentially be used to detect spammers. Employing these attributes, we apply machine learning to provide a heuristic for classifying an arbitrary video as either legitimate or spam. The machine learning algorithm is trained with our test collection. We then show that our approach succeeds at detecting much of the spam while only falsely classifying a small percentage of the legitimate videos as spam. Our results highlight the most important attributes for video response spam detection.