<p>Identifying influential nodes in social media networks is critical for improving information dissemination, digital marketing, and crisis response strategies. Traditional centrality measures exhibit key limitations: local metrics are fast but overlook structural context, while global metrics reflect network-wide influence but are computationally expensive and insensitive to local diversity. Hybrid approaches attempt to balance both, yet often neglect how a node’s influence depends on the exclusivity and structural positioning of its connections. To address these limitations, we propose Influence Disparity Centrality (IDC), a novel centrality measure that evaluates node importance not only by connection quantity, but by relative degree superiority and neighborhood non-redundancy. IDC prioritizes nodes that serve as non-obvious bridges between weakly connected regions, enhancing global diffusion with minimal redundancy. We validate IDC across heterogeneous datasets including Facebook networks, academic and student social graphs, and thematic online communities. Quantitative evaluations, which include Pearson and Spearman correlations, Kendall’s Tau under SIR simulations, as well as Epidemic Duration and Monotonicity, demonstrate that IDC consistently outperforms both traditional and hybrid measures in identifying structurally and functionally influential nodes. Unlike classical metrics which saturate early or misidentify hubs, IDC yields broader and more sustainable propagation in dynamic scenarios. Its capacity to reveal structurally strategic actors makes it particularly useful for large-scale influence modeling, viral outreach, and online behavioral interventions.</p>