Implementation of Machine Learning Algorithms for Detecting Bot and Fraudulent Accounts on Instagram Based on Public Profile Characteristics

Abstract

The rapid growth of Instagram as a social media platform has led to increased challenges related to fake accounts, including bots, spam, and scam profiles, which threaten the integrity and trustworthiness of online information. This study implements machine learning algorithms, particularly the Random Forest classifier, to detect and classify Instagram accounts into four categories: Real, Bot, Spam, and Scam, based on publicly available profile characteristics. A dataset of 15,000 Instagram profiles was collected and preprocessed, extracting features such as follower count, following count, posting frequency, and presence of profile information. The Random Forest model was trained and evaluated, achieving an accuracy of 97% with high precision and recall across all categories. Behavioral analysis revealed distinct patterns in following/follower ratios, posting activity, and mutual friends that differentiate genuine users from fake accounts. Feature importance ranking highlighted follower count as the most influential attribute for classification. The model demonstrated strong robustness through ROC and Precision-Recall curves, underscoring its effectiveness in a multiclass classification task. This approach not only enhances automated detection and moderation of malicious accounts but also contributes to maintaining a safer social media environment by mitigating misinformation and fraud. Future work could improve detection by incorporating temporal activity data, linguistic analysis, and real-time monitoring to adapt to evolving deceptive behaviors. Taken together, this study confirms the viability of machine learning methods in addressing the growing issue of fake accounts on Instagram, offering scalable and interpretable solutions for social media security.