Federating Governance: How Community Rules Scale with Mastodon Instances

2026-06-03

AI Summary

This study investigates how community governance rules evolve with instance scale in decentralized social platforms, addressing the lack of scalable guidance in volunteer-based moderation. By collecting and analyzing rule texts from Mastodon instances of varying sizes and applying computational social science methods—including thematic classification, readability assessment, and linguistic diversity metrics—the research identifies community size as the primary driver of rule complexity, outweighing differences in platform architecture. Findings reveal that harassment and hate speech remain central governance concerns across all scales; larger instances adopt more comprehensive but less readable rules; and federated interactions only marginally broaden rule scope without significantly affecting diversity or form. The observed governance trajectories closely resemble those of centralized platforms like Reddit, challenging the assumption that decentralization inherently fosters governance heterogeneity.

Abstract

The rise of decentralized social media platforms like Mastodon and Bluesky highlights the challenge of scaling self-governance and moderation. As communities grow, they face new issues that demand increasingly complex governance structures. However, as moderation is mainly volunteer-driven, there is limited formal guidance on how community rules and moderation practices should evolve with growth. This study investigates how moderation scale with Mastodon instances by analyzing community rules across servers of varying sizes. We categorize these rules to identify key governance priorities and find that these priorities are remarkably consistent across instance sizes: rules addressing problematic content, such as harassment, hate speech, and illegal content, dominate regardless of scale. While smaller communities focus on narrower sets of topics, larger servers maintain a more balanced coverage of a broad range of topics. Our analysis of rule formalization reveals that community size strongly predicts rule development. As instances grow, their rules become more extensive and topically diverse, but also exhibit lower readability and linguistic diversity. In contrast, external federation interactions have a limited role, mainly associated with a broader scope of rules without substantially affecting their diversity or form. These findings highlight the relative influence of internal versus external factors, suggesting that local scaling pressures outweigh network-level dynamics in decentralized social media governance. The scaling pattern observed on Mastodon resemble those previously identified on centralized platforms such as Reddit, suggesting that community size imposes fundamental constraints on self-governance that transcend platform architectures

Problem

• decentralized social media
• self-governance
• content moderation
• community rules
• scaling governance

Innovation

• decentralized social media
• community governance
• moderation scaling
• Mastodon instances
• rule formalization