Fake Account Identification in Social Networks

Abstract

Nowadays, the human influence often depends on the number of followers that an individual has in his/her own social media context. To this end, the presence of fake accounts is one of the most relevant problems and can potentially have a big impact on many real life and business activities. Fake followers are dangerous for social platforms, since they may alter concepts like popularity and influence, which might yield a strong impact on economy, politics, and society. Thus, it is necessary to devise new methodologies enabling the possibility to identify and characterize fake accounts. This work presents a novel technique to discriminate real accounts on social networks from fake ones. The technique exploits knowledge automatically extracted from big data to characterize typical patterns of fake accounts. We empirically evaluated the proposed technique on the Twitter social network, and achieved significant results in terms of discrimination capabilities.