Re-Fake: Classification of Fake Accounts on Online Social Media using the RNN Algorithm

Abstract

Online Social Network (OSN) is an application that enables public communication and
information sharing. However, fake accounts on OSN can spread false information with unknown sources. It is a challenging task to detect malicious accounts in a large OSN system. The existence of fake accounts or unknown accounts on OSN can be a serious problem in maintaining data privacy. Various communities have proposed many techniques to deal with fake accounts on OSN, including rules-based black-and-white techniques to learning approaches. Therefore, in this study we propose a classification model using RNN to detect fake accounts accurately and effectively. We carried out this research in several steps, including collecting the dataset, pre-processing, extraction, training our model using RNN. Based on the experimental results, our proposed model can produce higher accuracy than conventional learning models.