This study explores the critical role of artificial neural networks (ANNs) in detecting and mitigating the impact of fake accounts on social media platforms. As the prevalence of fake accounts continues to rise, posing significant threats to user trust, data integrity, and overall platform security, effective detection mechanisms are paramount. The proposed approach leverages advanced ANN architectures, such as feedforward and recurrent neural networks, to analyze user behavior, account characteristics, and interaction patterns. By training these models on diverse datasets comprising both legitimate and fraudulent accounts, we demonstrate the effectiveness of ANNs in identifying subtle patterns indicative of fake accounts. The results highlight significant improvements in detection accuracy and response time compared to traditional rule-based systems, showcasing the potential of deep learning techniques in enhancing social media security. This research underscores the importance of integrating ANNs into social media platforms to protect users and uphold the integrity of digital interactions, paving the way for more robust strategies in combating online impersonation and fraud.

This study explores the critical role of artificial neural networks (ANNs) in detecting and mitigating the impact of fake accounts on social media platforms. As the prevalence of fake accounts continues to rise