Muralidharan et al., "Federating Governance: How Community Rules Scale with Mastodon Instances" (2026)

Rasika Muralidharan, Yong-Yeol Ahn, and Bao Tran Truong, Proceedings of the ACM on Human-Computer Interaction (CSCW), accepted (2026)
arXiv

@article{muralidharan2026federating,
    author  = {Muralidharan, Rasika and Ahn, Yong-Yeol and Truong, Bao Tran},
    title   = {Federating Governance: How Community Rules Scale with Mastodon Instances},
    journal = {Proceedings of the ACM on Human-Computer Interaction},
    note    = {Accepted, CSCW 2026},
    year    = {2026},
}

Mastodon, Governance, Content moderation, Self governance, Fediverse

How do community rules change as a decentralized server grows? This paper categorizes the formal rules across Mastodon instances of varying sizes and asks what drives their evolution. Governance priorities turn out to be strikingly stable across scale—rules against harassment, hate speech, and illegal content dominate everywhere—but community size strongly predicts rule formalization: larger instances accumulate more extensive, topically diverse rules that are also less readable and less linguistically varied. Federation interactions, by contrast, play a limited role, suggesting that local scaling pressures outweigh network-level dynamics. The pattern echoes scaling effects found on centralized platforms like Reddit, hinting that community size constrains Self governance independently of platform architecture. It extends the rule-categorization work of Nicholson et al. (2023) by tying rule structure to instance size and federation.