Dual Parameters of Influence: Unveiling a Novel Centrality Measure for Social Media Networks

Abstract

This study introduces a novel centrality metric designed to identify influential actors in social media networks, based on two key parameters: relative degree superiority (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{x}$</tex> over y) and the minimization of shared neighbors. Our measure examines direct influence and also takes into account neighborhood non-redundancy. Both of these elements shape network dynamics. Centrality measures used in social media networks are different. They generally only consider direct connections. They often miss part of the network dynamics. We conducted theoretical analyses and empirical validations across various datasets to compare our metric against existing measures, highlighting its unique advantages in understanding network influence. According to our research, this measure improves the accuracy of influencing social media networks contributing to our understanding of the dynamic and structural characteristics of real-world systems.