Machine Learning-Based Fake Account Detection System: Instagram Case Study

People often create fake social media accounts to express themselves anonymously. However, these fake accounts can harm the reputation of individuals and businesses, resulting in fewer genuine likes and followers. Instagram, a top-rated social media platform often used for business and political engagement, suffers from the negative impacts of these accounts. This highlights the urgent need for a dependable system to identify whether Instagram accounts are genuine. This study investigated several
machine learning models for developing a fake account detection system. Single models, such as support vector machines, na�ve
Bayes, logistic regression, multilayer perceptron, and ensemble models based on bootstrap aggregating techniques and boosting,
were trained and tested. The training and testing processes were conducted using a 10-fold cross-validation to prevent
overfitting. The test results indicated that the adaptive and gradient boosting models achieved the best accuracy and an F1 score
of more than 92%, with precision surpassing 93%.