Distance-based customer detection in fake follower markets

Abstract As the Online Social Networks (OSNs) have become popular, more and more people want to increase their influence not only in the real world but also in the OSNs. However, increasing the influence in OSNs is time-consuming job, so some users want to find a shortcut to increase their relationships. The demand for quick increasement of relationship has led to the growth of the fake follower markets that cater to customers who want to grow their relationships rapidly. However, customers of fake follower markets cannot manipulate legitimate user’s relationship perfectly. Existing approaches explore node’s relationships or features to detect customers. But none of them combines the relationships and node’s features directly. In this article, we propose a model that directly combines the relationship and node’s feature to detect customers of fake followers. Specifically, we study the geographical distance for 1-hop-directional links using the nodes geographical location. Motivated by the difference of a distance ratio for 1-hop directional links, the proposed method is designed to generate a 1-hop link distance ratio, and classifies a node as a customer or not. Experimental results on a Twitter dataset demonstrate that the proposed method achieves higher performance than other baseline methods.