Classification of Instagram fake users using supervised machine learning algorithms

TL;DR

This paper presents a supervised machine learning approach to classify Instagram fake users, aiming to assist investigative agencies in identifying fraudulent profiles and combating online impersonation effectively.

Contribution

It introduces a novel application that leverages supervised machine learning algorithms to detect fake Instagram users, tailored for use by law enforcement and investigative agencies.

Findings

High accuracy in fake user classification

Effective integration with investigative workflows

Potential to reduce online impersonation incidents

Abstract

In the contemporary era, online social networks have become integral to social life, revolutionizing the way individuals manage their social connections. While enhancing accessibility and immediacy, these networks have concurrently given rise to challenges, notably the proliferation of fraudulent profiles and online impersonation. This paper proposes an application designed to detect and neutralize such dishonest entities, with a focus on safeguarding companies from potential fraud. The user-centric design of the application ensures accessibility for investigative agencies, particularly the criminal branch, facilitating navigation of complex social media landscapes and integration with existing investigative procedures