PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media

TL;DR

PluRule introduces a comprehensive benchmark for AI moderation in pluralistic social media communities, highlighting current models' struggles with nuanced, community-specific rule violations across multiple languages.

Contribution

The paper presents PluRule, a novel multimodal, multilingual benchmark for detecting diverse rule violations in social media communities, revealing limitations of current AI moderation models.

Findings

State-of-the-art models perform only slightly better than trivial baselines.

Larger models and more context yield marginal improvements.

Universal rules like civility are easier to detect.

Abstract

Social media are shifting towards pluralism -- community-governed platforms where groups define their own norms. What violates rules in one community may be perfectly acceptable in another. Can AI models help moderate such pluralistic communities? We formalize the task as a multiple-choice problem, mirroring how human moderators operate in the real world: given a comment and its surrounding context, identify which specific rule, if any, is violated. We introduce PluRule, a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities spanning 2,885 rules in 9 languages. Using this benchmark, we show that state-of-the-art vision-language models struggle significantly: even GPT-5.2 with high reasoning performs only slightly better than a trivial baseline. We also find that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect. Our results show that moderation of pluralistic communities on social media is a fundamental challenge for language models. Our code and benchmark are publicly available.