Harmonizing vs Polarizing Platform Influence Functions

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.