Algorithmic resolution of crowd-sourced moderation on X in polarized settings across countries (2506.15168v1)

Abstract: Social platforms increasingly transition from expert fact-checking to crowd-sourced moderation, with X pioneering this shift through its Community Notes system, enabling users to collaboratively moderate misleading content. To resolve conflicting moderation, Community Notes learns a latent ideological dimension and selects notes garnering cross-partisan support. As this system, designed for and evaluated in the United States, is now deployed worldwide, we evaluate its operation across diverse polarization contexts. We analyze 1.9 million moderation notes with 135 million ratings from 1.2 million users, cross-referencing ideological scaling data across 13 countries. Our results show X's Community Notes effectively captures each country's main polarizing dimension but fails by design to moderate the most polarizing content, posing potential risks to civic discourse and electoral processes.