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论文类型： 会议论文

第一作者： Zhang, Xianchao

合写作者： Bai, Haijun,Liang, Wenxin

发表时间： 2016-04-19

收录刊物： EI、CPCI-S

卷号： 9794

页面范围： 214-226

关键字： Semi-supervised learning; Social spam; Co-training; k-medoids

摘要： With the increasing popularity of social networking websites such as Twitter, Facebook, Sina Weibo and MySpace, spammers on them are getting more and more rampant. Social spammers always create a mass of compromised or fake accounts to deceive users and lead them to access malicious websites which contain illegal, pornography or dangerous information. As we all know, most of the studies on social spam detection are based on supervised machine learning which requires plenty of annotated datasets. Unfortunately, labeling a large number of datasets manually is a complex, error-prone and tedious task which may costs a lot of human efforts and time. In this paper, we propose a novel semi-supervised classification framework for social spam detection, which combines co-training with k-medoids. First we utilize k-medoids clustering algorithm to acquire some informative and presentative samples for labelling as our initial seeds set. Then we take advantage of the content features and behavior features of users for our co-training classification framework. In order to illustrate the effectiveness of k-medoids, we compare the performance with random selecting strategy. Finally, we evaluate the effectiveness of our proposed detection framework compared with several classical supervised algorithms.