AI-POWERED DETECTION OF FAKE FOLLOWERS AND SOCIAL MEDIA SPAMBOTS USING EXPLAINABLE MACHINE LEARNING

Abstract

The increasing use of social media platforms has resulted in the widespread presence of spambots and fake followers, which can manipulate online interactions, spread misinformation, and reduce the credibility of digital platforms. Detecting such malicious accounts has become a critical challenge due to their ability to imitate human behavior and evade traditional detection mechanisms. This study proposes an AI-powered system for detecting fake followers and social media spambots using explainable machine learning techniques. The system analyzes various user attributes, including account metadata, activity patterns, follower relationships, and posting behavior, to identify suspicious accounts. Machine learning algorithms are employed to classify accounts as genuine or malicious, while Explainable AI methods are integrated to provide transparent explanations for model predictions. This approach not only improves detection accuracy but also enhances interpretability and trust in the system. The proposed framework can assist social media platforms in maintaining authentic user engagement, preventing manipulation of online influence, and improving overall platform security.