De-sounding echo chambers: Simulation-based analysis of polarization dynamics in social networks

Abstract

As online social networks have become dominant platforms for public discourse worldwide, there is growing anecdotal evidence of a concurrent rise in social antagonisms. Yet, while the increase in polarization is evident, the extent to which these digital communication ecosystems are driving this shift remains elusive. A dominant scholarly perspective suggests that digital social media lead to the compartmentalization of information channels, potentially culminating in the emergence of echo chambers. However, a growing body of empirical research suggests that the mechanisms influencing ideological demarcation are more complex than a complete communicative decoupling of user groups. This study introduces two intertwined principles that elucidate the dynamics of digital communication: First, socio-cognitive biases of social group formation enforce internal congruence of ideological communities by demarcation from outsiders. Second, algorithmic personalization of content contributes to ideological network formation by creating social redundancy, wherein the same individuals frequently interact in various roles, such as authors, recipients, or disseminators of messages, leading to a surplus of shared ideological fragments. Leveraging these insights, we pioneer a computational simulation model, integrating machine learning based on behavioral data and established recommendation technologies, to explore the evolution of social network structures in digital exchanges. Utilizing advanced methods in opinion dynamics, our model uniquely captures both the algorithmic delivery and the subsequent dissemination of messages by users. Our findings reveal that in ambiguous debate scenarios, the dual components of our model are essential to accurately capture the emergence of social polarization. Consequently, our model offers a forward-looking perspective on the evolution of network communication, facilitating nuanced comparisons with empirical graph benchmarks.