Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance
<|reference_start|>Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance: 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.<|reference_end|>
@article{la cava2024safeguarding,
    title={Safeguarding Decentralized Social Media: LLM Agents for Automating
  Community Rule Compliance},
    author={Lucio La Cava, Andrea Tagarelli},
    journal={arXiv preprint arXiv:2409.08963},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08963},
    primaryClass={cs.CY cs.CL cs.HC physics.soc-ph}
}