On Crowd-Retweeting Spamming Campaign in Social Networks

Crowdsourcing is often used to solicit contributions from an online community for ideas, evaluation and opinions. However, spamming can pollute such a system and manipulate the results of crowdsourcing. For detection of those spammers, the training data used in previous studies is often derived by experts labeling collected data and manually identifying spammers. The reliability of such training data is questionable. In this paper, we utilize two web based service providers Zhubajie (ZBJ) and Sandaha (SDH) and obtain reliable data about the spammers. We use such data to investigate the crowd-retweeting spam in SinaWeibo. We analyze profile features, social relationship and retweeting behavior of such spammers. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social tie is different from the social relationship in other spam campaigns because of the unique retweeting features with the information cascade effect. Based on these findings, we propose retweeting-aware link based ranking algorithms to detect suspect spam accounts using seeds of identified spammers. Our evaluation shows that our algorithm is more effective than other link-based methods.