Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909315106865152 |
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| author | La Cava, Lucio Tagarelli, Andrea |
| author_facet | La Cava, Lucio Tagarelli, Andrea |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_08963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance La Cava, Lucio Tagarelli, Andrea Computers and Society Computation and Language Human-Computer Interaction Physics and Society 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. |
| title | Safeguarding Decentralized Social Media: LLM Agents for Automating Community Rule Compliance |
| topic | Computers and Society Computation and Language Human-Computer Interaction Physics and Society |
| url | https://arxiv.org/abs/2409.08963 |