Social media platforms such as Instagram are widely used for communication, networking, and content sharing. How- ever, the rapid growth of these platforms has also led to a signifi- cant increase in fraudulent or fake accounts. These accounts are often involved in activities such as spamming, phishing, spreading misinformation, and manipulating engagement metrics. Due to the large number of users and the dynamic behavior of social media platforms, manual identification of fake accounts becomes difficult and inefficient. This research proposes a machine learning-based approach to detect fake Instagram accounts using profile-based features. Various attributes such as follower-following ratio, number of posts, engagement behavior, profile completeness, and other pro- file characteristics are analyzed. The Random Forest classification algorithm is used to distinguish between real and fake accounts. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The experimental results demonstrate that the proposed approach can effectively identify fake accounts and contribute to improving the reliability and security of social media platforms.
Khamkar et al. (Thu,) studied this question.