Classification of Instagram fake users using supervised machine learning algorithms
This addresses the issue of online impersonation and fraud for companies and criminal investigators, but appears incremental as it applies existing machine learning methods to a specific social media context.
The paper tackled the problem of fraudulent profiles on Instagram by developing an application to detect fake users using supervised machine learning algorithms, aiming to protect companies from fraud and assist investigative agencies.
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