# Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance

arXiv: https://arxiv.org/abs/2409.08963 · HTML: https://mlsanity.com/paper/2409.08963/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 16 of 16)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Prompting Frameworks for Large Language Models: A Survey](https://mlsanity.com/paper/2311.12785/) (2023)
- [Quarantined! Examining the Effects of a Community-Wide Moderation Intervention on Reddit](https://mlsanity.com/paper/2009.11483/) (2020)
- [Multi-task dialog act and sentiment recognition on Mastodon](https://mlsanity.com/paper/1807.05013/) (2018)
- ["I Won the Election!": An Empirical Analysis of Soft Moderation Interventions on Twitter](https://mlsanity.com/paper/2101.07183/) (2021)
- [Toxicity in the Decentralized Web and the Potential for Model Sharing](https://mlsanity.com/paper/2204.12709/) (2022)
- [How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events](https://mlsanity.com/paper/2209.07614/) (2022)
- [Challenges in the Decentralised Web: The Mastodon Case](https://mlsanity.com/paper/1909.05801/) (2019)
- [Understanding the growth of the Fediverse through the lens of Mastodon](https://mlsanity.com/paper/2106.15473/) (2021)
- [Exploring Content Moderation in the Decentralised Web: The Pleroma Case](https://mlsanity.com/paper/2110.13500/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025)
- [LLM Agents in Law: Taxonomy, Applications, and Challenges](https://mlsanity.com/paper/2601.06216/) (2026)
- [Overcoming Vision Language Model Challenges in Diagram Understanding: A Proof-of-Concept with XML-Driven Large Language Models Solutions](https://mlsanity.com/paper/2502.04389/) (2025)
- [A Multi-Stage Workflow for the Review of Marketing Content with Reasoning Large Language Models](https://mlsanity.com/paper/2601.06054/) (2025)
- [Are Open-Weight LLMs Ready for Social Media Moderation? A Comparative Study on Bluesky](https://mlsanity.com/paper/2602.05189/) (2026)

## Abstract (arXiv)

Ensuring content compliance with community guidelines is crucial for maintaining healthy online social environments. However, traditional human-based compliance checking struggles with scaling due to the increasing volume of user-generated content and a limited number of moderators. Recent advancements in Natural Language Understanding demonstrated by Large Language Models unlock new opportunities for automated content compliance verification. This work evaluates six AI-agents built on Open-LLMs for automated rule compliance checking in Decentralized Social Networks, a challenging environment due to heterogeneous community scopes and rules. Analyzing over 50,000 posts from hundreds of Mastodon servers, we find that AI-agents effectively detect non-compliant content, grasp linguistic subtleties, and adapt to diverse community contexts. Most agents also show high inter-rater reliability and consistency in score justification and suggestions for compliance. Human-based evaluation with domain experts confirmed the agents' reliability and usefulness, rendering them promising tools for semi-automated or human-in-the-loop content moderation systems.