Digital Governance Analytics: Unveiling Platform Moderation Data Structures

Abstract

This paper investigates how major online platforms influence and reshapes the flow of digital information by examining their content moderation practices. It outlines a methodology for observing platform interventions–categorized as user suspensions, content flagging, and visibility reduction–through the collection and analysis of publicly available social media data. We supplement this audit with a BERT-based classifier, finding a 92% alignment between model-predicted misinformation and platform interventions, and employ causal inference (Difference-in-Differences) to estimate the impact of labeling. The paper demonstrates the discernible patterns and inherent structures that emerge from these moderation actions on platforms like Facebook, Twitter, and YouTube. The work highlights the challenges in systematically understanding these data-driven governance mechanisms and offers insights for external observers interested in the organization of online information.