Identification of Social Media Accounts Using Big Data Analysis on Social Media Platforms

Abstract The rapid growth of social media platforms has led to an unprecedented increase in user-generated data, creating significant challenges in accurately identifying and correlating social media accounts across platforms. Traditional detection approaches, which primarily rely on content-based analysis or small-scale heuristic methods, often suffer from limited scalability and reduced effectiveness in handling large and heterogeneous datasets. To address these limitations, this research proposes a big data–driven framework for identifying and linking social media accounts using comprehensive behavioral analysis. The proposed approach leverages multiple data dimensions, including user behavioral patterns, metadata attributes, activity logs, and network relationships, to establish robust identity correlations across platforms. A distributed processing architecture based on Hadoop and Apache Spark is implemented to enable scalable, real-time analysis of massive social media datasets. Machine learning models are developed to learn complex patterns and relationships within the data, facilitating accurate cross-platform identity correlation and detection of suspicious or related accounts.Experimental evaluations demonstrate that the proposed framework significantly improves identification accuracy and scalability compared to traditional content-centric methods. The results highlight the effectiveness of integrating big data analytics with machine learning techniques for large-scale social media account identification. This research contributes a scalable and efficient solution for enhancing trust, security, and integrity in online social networks, making it suitable for real-time deployment in modern social media environments. y.