Technical Summary: Federating Governance on Mastodon

Problem Statement

Decentralized social media platforms like Mastodon and Bluesky offer an alternative to centralized governance by distributing authority across independently operated servers (instances). While this model addresses the limitations of centralized platforms regarding diverse cultural norms, it introduces significant challenges in scaling self-governance. Unlike centralized platforms that can invest in automated infrastructure and standardized training as they grow, Mastodon instances rely almost entirely on volunteer moderators. As communities expand, they face increased content volumes, diverse user behaviors, and complex coordination needs without proportional resource increases.

A critical gap exists in understanding how governance scales in these volunteer-driven, federated environments. Prior research on centralized platforms (e.g., Reddit) suggests that larger communities develop more extensive, formalized rule sets. However, it remains unclear whether these scaling patterns hold in decentralized systems where communities must adapt independently. Furthermore, it is unknown to what extent external factors, such as federation interactions with other instances, influence local rule development compared to internal community size.

Methodology

The study employs a large-scale, multi-lingual quantitative analysis of community rules across the Mastodon ecosystem.

Data Collection:

• Dataset: The authors collected data in August 2024 using the instances.social API, filtering for 6,660 active Mastodon instances with at least one rule.
• Scale: The dataset includes 28,910 rules spanning instances from size 1 to over 2 million users.
• Exclusions: The "default" instance (mastodon.social) was excluded due to its unique status and massive scale (>10M users). Three statistical outliers were removed manually.
• Preprocessing: Non-English rules (approx. one-third of the dataset) were translated to English using Google Translate, with quality validated via back-translation and readability checks.

Analytical Framework:

1. Rule Categorization:
   • A subset of 1,100 unique rules was inductively coded and mapped to a modified ABCD framework (Action, Behavior, Content, Distribution), originally designed for influence operations.
   • Topics were grouped into four categories: Action (e.g., automated tools), Behavior (e.g., harassment, doxxing), Content (e.g., hate speech, illegal content), and Distribution (e.g., spam, copyright).
   • LLM Classification: A few-shot prompt was developed and validated on GPT-4o-mini (76% accuracy against manual annotations) to classify all 28,910 rules into these categories.
2. Metric Definition:
   • Rule Diversity: Measured using Shannon Entropy across the four ABCD categories.
   • Lexical Features: Analyzed using Word Count, Rule Count, Type-Token Ratio (TTR) for lexical diversity, and Flesch-Kincaid Reading Ease (FRE) for readability.
   • Independent Variables: Instance Size (user count) and Federation Degree (number of federating connections).
3. Statistical Modeling:
   • Regression analyses were performed to predict rule formalization outcomes.
   • Models: Negative Binomial regression for count data (word count, rule count, topic count); Beta regression for ratio data (TTR); OLS with robust standard errors for readability scores.
   • Controls: Analyses controlled for instance age and used engagement volume (statuses) as a robustness check for community size.

Key Results

1. Consistency of Governance Priorities

• Regardless of instance size, the dominant rule themes are Hate Speech, Harassment, and Illegal Content.
• This indicates a "normative convergence" where decentralized instances align on a shared floor of harm reduction, mirroring priorities found in centralized platforms.

2. Scaling Patterns of Rule Formalization

• Volume and Scope: As instance size increases, the number of rules, word count, and topic diversity increase significantly. Larger instances maintain a more balanced coverage across the ABCD categories, whereas smaller instances focus disproportionately on a narrower set of topics.
• Linguistic Complexity: Counterintuitively, larger instances exhibit lower readability (lower FRE scores) and lower lexical diversity (lower TTR).
  • Interpretation: Larger communities appear to adopt more standardized, formal, and specialized language to reduce ambiguity, rather than simplifying rules for broader comprehension.
• Correlation Strength: Instance size is a strong, statistically significant predictor of all rule formalization metrics (e.g., a tenfold increase in users correlates with a 53% increase in word count and a 34% increase in rule count).

3. Internal vs. External Drivers

• Community Size: The primary driver of rule development. Local scaling pressures dictate the complexity and scope of governance.
• Federation Degree: External federation interactions have a limited role. While a higher degree of federation is weakly associated with a broader scope of rules (more rule count), it does not significantly affect linguistic diversity, readability, or the fundamental structure of rule formalization.
• Robustness: These patterns persist even when controlling for instance age and when using engagement metrics instead of user counts. The findings are not significantly confounded by the 2022 Twitter/X migration or specific topical focuses of instances.

Significance and Claims

The paper argues that the scaling dynamics of self-governance are fundamental constraints that transcend platform architecture.

• Trans-Architectural Similarity: The scaling patterns observed on Mastodon (volunteer-driven, decentralized) closely resemble those previously identified on centralized platforms like Reddit. This suggests that as communities grow, the need for formalized, extensive, yet increasingly complex governance is an inherent challenge of social coordination, not merely a byproduct of centralized corporate management.
• Local Primacy: In federated systems, local scaling pressures outweigh network-level dynamics. Governance practices are shaped primarily by internal community needs rather than external federation interactions.
• Design Trade-offs: The study highlights a tension between precision and accessibility. As instances grow, rules become more precise and standardized (lower TTR) but less readable. This creates a potential barrier for user compliance and understanding, particularly for newcomers in large, diverse communities.

The authors conclude that while decentralization changes where governance decisions are made, it does not eliminate the structural challenges of scaling self-governance. They suggest that platform designers should provide tools (e.g., size-calibrated rule templates, readability checkers) to help volunteer administrators manage the increasing complexity of governance as their communities grow.