# Let Community Rules Be Reflected in Online Content Moderation

arXiv: https://arxiv.org/abs/2408.12035 · HTML: https://mlsanity.com/paper/2408.12035/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges](https://mlsanity.com/paper/2403.00784/) (2024)
- [To Act or React: Investigating Proactive Strategies For Online Community Moderation](https://mlsanity.com/paper/1906.11932/) (2019)

## Built on by (most cited first, 9 of 9)

- [Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison](https://mlsanity.com/paper/2412.05536/) (2024)
- [Comprehensive Overview of Artificial Intelligence Applications in Modern Industries](https://mlsanity.com/paper/2409.13059/) (2024)
- [DyConfidMatch: Dynamic Thresholding and Re-sampling for 3D Semi-supervised Learning](https://mlsanity.com/paper/2411.08340/) (2024)
- [ChromouVQA: Benchmarking Vision-Language Models under Chromatic Camouflaged Images](https://mlsanity.com/paper/2512.05137/) (2025)
- [MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation](https://mlsanity.com/paper/2601.06519/) (2026)
- [Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)](https://mlsanity.com/paper/2409.16590/) (2024)
- [Generative Pre-trained Ranking Model with Over-parameterization at Web-Scale (Extended Abstract)](https://mlsanity.com/paper/2409.16594/) (2024)
- [CROPS: A Deployable Crop Management System Over All Possible State Availabilities](https://mlsanity.com/paper/2411.06034/) (2024)
- [Asking For It: Question-Answering for Predicting Rule Infractions in Online Content Moderation](https://mlsanity.com/paper/2510.06350/) (2025)

## Abstract (arXiv)

Content moderation is a widely used strategy to prevent the dissemination of irregular information on social media platforms. Despite extensive research on developing automated models to support decision-making in content moderation, there remains a notable scarcity of studies that integrate the rules of online communities into content moderation. This study addresses this gap by proposing a community rule-based content moderation framework that directly integrates community rules into the moderation of user-generated content. Our experiment results with datasets collected from two domains demonstrate the superior performance of models based on the framework to baseline models across all evaluation metrics. In particular, incorporating community rules substantially enhances model performance in content moderation. The findings of this research have significant research and practical implications for improving the effectiveness and generalizability of content moderation models in online communities.