Detecting Spammers with a Bidirectional Vote Algorithm Based on Statistical Features in Microblogs

The existing work mainly focuses on spammers detection in microblogs based on explicit features, such as the interval of tweets, the ratio of mentions in tweets, the ratio of URLs in tweets, and so on. In this paper, the DirTriangleC algorithm which counts local triangles is developed in order to detect the implicit spammers, based on the following directed network. Moreover, the AttriBiVote algorithm, which classifies users by the bidirectional propagation of the trust and statistical features of neighbors' users, is put forward. Experiments are conducted on a real dataset from Twitter containing about 0.26 million users and 10 million tweets, and experimental results show that the method in this paper is more effective than other methods of statistical features. About 83.7% of dead accounts are discovered by the DirTriangleC algorithm, and the number of potential spammers by the DirTriangleC algorithm is about treble others' by explicit features. Moreover, the number of spammers by the AttriBiVote algorithm is more than that of approximation spammers by statistical features. And the precision of our method is higher than that of the methods by the interval of tweets, the ratio of mentions in tweets, and the ratio of URLs in tweets. Finally, the time cost of our method is analyzed.