Quantitative Analysis of Social Influence & Digital Piracy Contagion with Differential Equations on Networks

TL;DR

This paper models the spread of online piracy using differential equations on social networks, analyzing thresholds and control strategies to inform awareness campaigns and reduce piracy.

Contribution

It introduces a novel network-based differential equation model for online piracy, incorporating media influence and heterogeneity to identify control strategies.

Findings

Identified thresholds for piracy persistence in social networks.

Demonstrated effectiveness of media campaigns in reducing piracy.

Provided a mathematical framework for analyzing online piracy dynamics.

Abstract

Though the studies of social contagions are regularly borrowing network models to study the propagation of social influences and opinions to include social heterogeneity. Such studies provide valuable insights regarding these, but the social network structures cannot be well explored in their study. In this research, we methodically study the trends in online piracy with a continuous ODE approach and differential equations on graphs, to have a clear comparative view. We first formulate a compartmental model to mathematically study bifurcations and thresholds, and later move on with a network-based analysis to illustrate the proliferation of online piracy dynamic with an epidemiological approach over a social network. We figure out a solution for this online piracy problem by developing awareness among individuals by introducing media campaigns which could be a useful factor for the eradication and control of online piracy. Next, using degree-block approximation, network analysis has been performed to investigate the phenomena from a heterogeneous approach and to derive the threshold condition for the persistence of piracy in the population in a steady state. Based on the behavioral responses of individuals in a society due to the effect of media, we examine the system through the aid of realistic parameter selection to better understand the complexity of the dynamics and propose control strategies.