Hybrid Approach for Fake Profile Identification on Social Media

Abstract

Millions of people use social media platforms like Twitter, Facebook, and Instagram all around the world. People are drawn to these social media sites, and as the prevalence of social media grow, so do the security and privacy concerns that come with it. Nowadays, it is critical to ensure that we are following the correct social media account or purchasing a product from the actual consumer, as malicious users can be extremely harmful. This paper proposes a hybrid method for detecting fake social media user accounts. For detecting fake accounts, it makes use of the Instagram social media platform’s dataset. There are two steps to the hybrid approach. The first stage is to use Principal Component Analysis (PCA) which turn original variables into new uncorrelated variables, and the second stage is to use various classification algorithms, in the second stage five algorithms are used to obtain accurate results. Fake profiles are detected using naive Bayes, artificial neural networks (ANN), support vector machine (SVM), logistic regression, and K-nearest neighbors (KNN) algorithms. When the classification performances of these approaches are compared, the artificial neural network outperforms the others.