Machine Learning-Based Identification of False Profiles

In the current digital era, phony profiles are a problem for social media sites like Instagram. An important problem that has surfaced on these platforms is the increase in phony profiles, which can result in fraud, false information, and privacy violations, among other problems. Because of the huge number of users, manually identifying bogus profiles takes a lot of effort and is frequently ineffective. This study explores the use of machine learning approaches for Instagram false profile detection. The study uses Random Forest methods and artificial neural networks (ANN) to identify patterns suggestive of fraudulent accounts by utilizing a large dataset. The accuracy with which these approaches can identify bogus profiles is evaluated through a thorough process of experimentation and evaluation. The outcomes demonstrate how well ANN and Random Forest work to differentiate between real