A novel framework for internet of knowledge protection in social networking services

With the increasing number of users on Social Networking Service (SNS), the Internet of knowledge shared on it is also increasing. Given such enhancement of Internet of knowledge on SNS, the probability of spreading spammers on it is also increasing day by day. Several traditional machine-learning methods, such as support vector machines and naïve Bayes, have been proposed to detect spammers on SNS. Note, however, that these methods are not efficient due to some issues, such as lower generalization performance and higher training time. An Extreme Learning Machine (ELM) is an efficient classification method that can provide good generalization performance at higher training speed. Nonetheless, it suffers from overfitting and ill-posed problem that can degrade its generalization performance. In this paper, we propose a Bagging ELM-based spammer detection framework that identifies spammers in SNSs with the help of multiple ELMs that we combined using the bagging method. We constructed a labeled dataset of the two most prominent SNSs -- Twitter and Facebook -- to evaluate the performance of our framework. The evaluation results show that our framework obtained higher generalization performance rate of 99.01% for the Twitter dataset and 99.02% for the Facebook datasets, while required a lower training time of 1.17 s and 1.10s, respectively.