Sina-Weibo Spammer Detection with GBDT

In China, Sina-Weibo, with its rising popularity as a microblogging website, has inevitably attracted the attention of spammers. Spammers use myriad of techniques to evade security mechanisms and post spam messages, which are either unwelcome advertisements for the victim or lure victims in to clicking malicious URLs embedded in spam tweets. With the extensive application of machine learning in social media mining and Sina-Weibo’s development, we get many new ideas for the spammers detection. In this paper, we first make a comprehensive analysis specifically aiming at some new Sina-Weibo features rather than other social media, we further design a new feature set to detect spammers. We grab a large amount of Sina-Weibo data on the Internet and train the classifier with the algorithm GBDT. Through our experiments, we show that our new designed features are much more effective than some existing detector. And GBDT also has been significantly improved in both the accuracy and the FP-rate.