Harmonizing vs Polarizing Platform Influence Functions

TL;DR

This paper models opinion dynamics on social platforms to understand how algorithms and social influence contribute to polarization, offering insights into mitigating divisiveness in online discourse.

Contribution

It introduces a differential equation model capturing platform influence and social influence, analyzing conditions for opinion convergence or polarization.

Findings

Platform influence functions significantly affect opinion polarization.

Certain social network structures promote consensus over polarization.

Strategies can be identified to reduce polarization based on model insights.

Abstract

We investigate the dynamics of opinion formation on social networking platforms, focusing on how individual opinions, influenced by both social connections and platform algorithms, evolve. We model this process using a differential equation, accounting for both peer influence and the platform's content curation based on user preferences. Our primary aim is to analyze how these factors contribute to opinion polarization and identify potential strategies for its mitigation. We explore the conditions under which opinions converge to a consensus or remain polarized, emphasizing the role of the platform's influence function. Our findings in two-agent, complete graphs, and stochastic block model provide insights into the impact of social media algorithms on public discourse and offer a framework for understanding how polarization can be avoided.