# "Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies

arXiv: https://arxiv.org/abs/2405.05225 · HTML: https://mlsanity.com/paper/2405.05225/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2024.

## Built on by (most cited first, 11 of 11)

- [Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations](https://mlsanity.com/paper/2503.01623/) (2025)
- [Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey](https://mlsanity.com/paper/2503.01513/) (2025)
- [Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators](https://mlsanity.com/paper/2411.05854/) (2024)
- [The Gray Area: Characterizing Moderator Disagreement on Reddit](https://mlsanity.com/paper/2601.01620/) (2026)
- [Adapting to Evolving Adversaries with Regularized Continual Robust Training](https://mlsanity.com/paper/2502.04248/) (2025)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation](https://mlsanity.com/paper/2507.04350/) (2025)
- [When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms](https://mlsanity.com/paper/2601.11634/) (2026)
- [Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch](https://mlsanity.com/paper/2506.07667/) (2025)
- [Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions](https://mlsanity.com/paper/2604.16765/) (2026)
- [Beyond Effectiveness: A Multi-Criteria Framework for Comparing Practical Socio-Technical Interventions](https://mlsanity.com/paper/2608.20649/) (2026)

## Abstract (arXiv)

Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. Each platform has thus developed bespoke content moderation policies, but there is little work towards a comparative understanding of these policies across platforms and topics. This paper presents the first systematic study of these policies from the 43 largest online platforms hosting user-generated content, focusing on policies around copyright infringement, harmful speech, and misleading content. We build a custom web-scraper to obtain policy text and develop a unified annotation scheme to analyze the text for the presence of critical components. We find significant structural and compositional variation in policies across topics and platforms, with some variation attributable to disparate legal groundings. We lay the groundwork for future studies of ever-evolving content moderation policies and their impact on users.