Comments for a product or a news article are rapidly growing and became a medium of measuring quality products or services. Consequently, spammers have been emerged in this area to bias them toward their favor. In this paper, we propose an efficient spammer detection method using structural rank of author specific term-document matrices. The use of structural rank was found effective and far faster than similar methods.
Our work is close to social media spam detection as they usually deal with short documents with large number of authors. The approaches are slightly different from traditional spam detection which focuses emails or websites. @cite_13 is a good survey of dealing with spam in social media.
@cite_13
2023271960 2107541186 2008347539 196347582
We examine the problem of collective attention spam, in which spammers target social media where user attention quickly coalesces and then collectively focuses around a phenomenon. Compared to many existing spam types, collective attention spam relies on the users themselves to seek out the content -- like breaking news, viral videos, and popular memes -- where the spam will be encountered, potentially increasing its effectiveness and reach. We study the presence of collective attention spam in one popular service, Twitter, and we develop spam classifiers to detect spam messages generated by collective attention spammers. Since many instances of collective attention are bursty and unexpected, it is difficult to build spam detectors to pre-screen them before they arise; hence, we examine the effectiveness of quickly learning a classifier based on the first moments of a bursting phenomenon. Through initial experiments over a small set of trending topics on Twitter, we find encouraging results, suggesting that collective attention spam may be identified early in its life cycle and shielded from the view of unsuspecting social media users. Twitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34K trending topics and 20million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-of-the-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts. The popularity of microblogging platforms, such as Twitter, makes them important for information dissemination and sharing. However, they are also recognized as ideal places by spammers to conduct social spamming. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages in microblogging is of great value. Existing studies mainly regard social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Combining social spammer detection with spam message detection has the potential to boost the performance of each task. In this paper, we propose a unified framework for social spammer and spam message co-detection in microblogging. Our framework utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users as well as the connections between messages, and incorporate them into our framework as regularization terms over the prediction results. Besides, we introduce an efficient optimization method to solve our framework. Extensive experiments on a real-world microblog dataset demonstrate that our framework can significantly and consistently improve the performance of both social spammer detection and spam message detection. As online social networks acquire a larger user base, they also become more interesting targets for spammers. Spam can take very different forms on social web sites and can not always be detected by analyzing textual content. However, the platform’s social nature also offers new ways of approaching the spam problem. In this work we analyze a user’s friends and followers to gain information on him. Next, we evaluate them using different metrics to determine the amount of trust his peers give him. We use the Twitter microblogging platform for this case study.