Machine Learning And Data Science based Fake Account Detection

Abstract

Nowadays the usage of digital technology
have been increasing exponentially. At the same time
the rate of malicious users have been increasing.
Online social sites like Facebook and Twitter attract
millions of people globally. This interest in online
networking has opened to various issues including the
risk of exposing false data by creating fake accounts
resulting in the spread of malicious content. Fake
accounts are a popular way to forward spam, commit
fraud, and abuse through online social network. These
problems need to be tackled in order to give the user a
reliable online social network. In this paper, we are
using different ML algorithms like Support Vector
Machine (SVM), Logistic Regression (LR), Random
Forest (RF) and K-Nearest Neighbours (KNN). Along
with these algorithms we have used two different
normalization techniques such as Z-Score, and Min-
Max, to improve accuracy. We have implemented it to
detect fake Twitter accounts and bots. Our approach
achieved high accuracy and true positive rate for
Random Forest and KNN. Keywords: Data mining,
Classification, Logistic Regression, Support Vector
Machine, KNearest Neighbours, Random forest,
Normalization.