Social Media Virality and the Diffusion of Innovation: A Network-Econometric Approach

Abstract

The rapid expansion of social media platforms has fundamentally transformed the mechanisms through which innovations diffuse across populations. Virality, once considered a stochastic outcome of exposure, has evolved into a structurally mediated, network-driven phenomenon shaped by social interactions and platform algorithms. This study examines social media virality through the theoretical lens of diffusion of innovation, employing a network-econometric framework to capture both individual adoption behavior and systemic propagation effects. By integrating network analysis with econometric modeling, the paper accounts for peer effects, endogenous network formation, and algorithmic amplification mechanisms that influence diffusion dynamics. Social media networks are conceptualized as weighted and dynamic graphs, enabling the analysis of how network characteristics—such as centrality, clustering, and homophily—affect adoption thresholds and diffusion speed. The proposed framework addresses key identification challenges in social interaction models, including simultaneity and correlated unobservables, through structural modeling and instrumental variable strategies. This research contributes to the literature by linking micro-level decision-making to macro-level diffusion patterns and offers implications for innovation strategy, digital platform governance, and causal inference in networked environments.