A cascading framework for uncovering spammers in social networks

With tremendous popularity, OSNs have become the most important platform for marketing and advertising during the past years. Meanwhile, spamming has already become a very serious problem in OSNs, drawing the attention of both academic and industry communities. In this paper, we investigate the problem of spammer detection from the perspective of user behaviors, including relation creation, user activeness, user interaction and tweet content. We quantitatively explore their correlations with spammer detection and find that tweet content is the most important factor for spammer detection, followed by relation creation. Based on these behavior factors, we propose a novel cascading framework CWB-SPAM for spammer detection in OSNs. Experiments on dataset crawled from Sina Microblog show that the proposed algorithm outperforms over all classical algorithms we investigated in terms of F-score¹. Experiments also demonstrate that as a probabilistic classification model, the proposed CWB-SPAM has a good ranking quality. It enables the OSN operators to make tradeoff between precision and recall easily so that the proposed algorithm can be used in different scenarios. Besides, we also note that the proposed framework can be used in other probabilistic binary classification models and thus applied in more scenarios.