Co-detecting social spammers and spam messages in microblogging via exploiting social contexts

Microblogging websites, such as Twitter, have become popular platforms for information dissemination and sharing. However, they are also full of spammers who frequently conduct social spamming on them. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages is of great value to both microblogging users and websites. Existing studies usually treat social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong inherent connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Thus combining social spammer detection with spam message detection has the potential to boost the performance of both tasks. In this paper, we propose a unified approach for social spammer and spam message co-detection in microblogging. Our approach utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users and the connections between messages to refine detection results. We regard these social contexts as the graph structure over the detection results and incorporate them into our approach as regularization terms. Besides, we introduce an efficient optimization algorithm to solve the model of our approach and propose an accelerated method to tackle the most time-consuming step. Extensive experiments on a real-world microblog dataset demonstrate that our approach can improve the performance of both social spammer detection and spam message detection effectively and efficiently.