Instagram Fake and Automated Account Detection

TL;DR

This paper addresses the problem of detecting fake and automated Instagram accounts using machine learning, introduces new datasets, and achieves high classification accuracy to combat fake engagement in social networks.

Contribution

It provides the first publicly available datasets for fake and automated Instagram accounts and applies various machine learning methods including a novel cost-sensitive genetic algorithm.

Findings

86% accuracy for fake account detection

96% accuracy for automated account detection

Introduces new datasets for research

Abstract

Fake engagement is one of the significant problems in Online Social Networks (OSNs) which is used to increase the popularity of an account in an inorganic manner. The detection of fake engagement is crucial because it leads to loss of money for businesses, wrong audience targeting in advertising, wrong product predictions systems, and unhealthy social network environment. This study is related with the detection of fake and automated accounts which leads to fake engagement on Instagram. Prior to this work, there were no publicly available dataset for fake and automated accounts. For this purpose, two datasets have been published for the detection of fake and automated accounts. For the detection of these accounts, machine learning algorithms like Naive Bayes, Logistic Regression, Support Vector Machines and Neural Networks are applied. Additionally, for the detection of automated accounts, cost sensitive genetic algorithm is proposed to handle the unnatural bias in the dataset. To deal with the unevenness problem in the fake dataset, Smote-nc algorithm is implemented. For the automated and fake account detection datasets, 86% and 96% classification accuracies are obtained, respectively.