How Many Zombies Around You?

Recent years have witnessed the explosive growth of online social media. Weibo, a famous "Chinese Twitter", has attracted over half billion users in less than four years. Among them are zombie users or bogus users, who are seemingly active common users but actually marionettes manipulated by intelligent software for economic interests. To probe such users thus becomes critically important for a healthy Weibo, but the existing studies along this line are still in initial stage due to the serious lack of labeled zombies and the limited attributes for user profiling. In light of this, in this paper, we figure out a commercial way for training set labeling, and propose a two-stage cascading model called ProZombie for zombie user recognition. ProZombie decomposes the training/predicting process into fast and refined phases in cascade, which greatly improves the modeling efficiency without sacrificing the accuracy. Moreover, 35 attributes including 16 newly proposed ones are employed for a panoramic description of Weibo users. Experiments on real-world labeled Weibo users demonstrate the effectiveness and efficiency of ProZombie. More interestingly, two case studies based on ProZombie successfully unveil the zombies hidden around common users, and their impact to information propagation on Weibo. To our best knowledge, this study is among the first to quantify these interesting observations on Weibo.