No 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.

Traditionally, users have discovered information on the Web by browsing or searching. Recently, word-of-mouth has emerged as a popular way of discovering the Web, particularly on social networking sites like Facebook and Twitter. On these sites, users discover Web content by following URLs posted by their friends. Such word-of-mouth based content discovery has become a major driver of traffic to many Web sites today. To better understand this popular phenomenon, in this paper we present a detailed analysis of word-of-mouth exchange of URLs among Twitter users. Among our key findings, we show that Twitter yields propagation trees that are wider than they are deep. Our analysis on the geolocation of users indicates that users who are geographically close together are more likely to share the same URL.

No 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.

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.

This article characterizes video-based interactions that emerge from YouTube's video response feature, which allows users to discuss themes and to provide reviews for products or places using much richer media than text. Based on crawled data covering a representative subset of videos and users, we present a characterization from two perspectives: the video response view and the interaction network view. In addition to providing valuable statistical models for various characteristics, our study uncovers typical user behavioral patterns in video-based environments and shows evidence of opportunistic behavior.

This paper seeks understanding the user behavior in a social network created essentially by video interactions. We present a characterization of a social network created by the video interactions among users on YouTube, a popular social networking video sharing system. Our results uncover typical user behavioral patterns as well as show evidences of anti-social behavior such as selfpromotion and other types of content pollution.