Detecting spamming groups in social media based on latent graph

Spammers in microblogging services aim to disseminate unuseful or misleading information, which leads to poor user experience and negative impact on the ecosystem of social media platform. Individual spammer detection, based on content and social network information, has been proposed to alleviate this predicament. However, most of the time spamming behavior is collaboratively conducted by a group of users, referred to as spamming group. In this paper, we propose to detect spamming groups in microblogging services. At the first step, we proposed RP-LDA to extract user features and find user groups within which users share similar retweeting behavior. Then, the degrees of individual users that are spammers are calculated by using a semi-supervised label propagation procedure. Finally, we determine the spamming groups using mixed membership distribution of users. Empirical studies over a real-life dataset demonstrate the effectiveness of our method and show that it can outperform the baseline.