Attribute selection for improving spam classification in online social networks: a rough set theory-based approach

As online social network (OSN) sites become increasingly popular, they are targeted by spammers who post malicious content on the sites. Hence, it is important to filter out spam accounts and spam posts from OSNs. There exist several prior works on spam classification on OSNs, which utilize various features to distinguish between spam and legitimate entities. The objective of this study is to improve such spam classification, by developing an attribute selection methodology that helps to find a smaller subset of the attributes which leads to better classification. Specifically, we apply the concepts of rough set theory to develop the attribute selection algorithm. We perform experiments over five different spam classification datasets over diverse OSNs and compare the performance of the proposed methodology with that of several baseline methodologies for attribute selection. We find that, for most of the datasets, the proposed methodology selects an attribute subset that is smaller than what is selected by the baseline methodologies, yet achieves better classification performance compared to the other methods.