Abstract

The advent of social media has catalyzed a paradigm shift in the way information is disseminated and consumed, giving rise to novel phenomena such as viral trends and information diffusion. This review article provides an in-depth scholarly examination of network science as applied to social media analysis, focusing on the mathematical formulations, algorithmic techniques, and interdisciplinary methodolo-gies that underpin the field. By exploring graph theory, community detection, scale-free networks, centrality measures, machine learning applications, and cultural influences, this study offers a comprehensive and nuanced understanding of network structures and dynamics. As a top-tier contribution to the field of computer science, this review serves as a nexus for the interdisciplinary study of network science, providing valuable insights and directions for future research in the analysis of information diffusion and viral trends within social media platforms.