"Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies
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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.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 May 2024.
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Citing papers per year: 2024: 1; 2025: 6; 2026: 4.
11 papers.
- 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variationscited by 13
- 2025Evaluation and Facilitation of Online Discussions in the LLM Era: A Surveycited by 9
- 2024Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotatorscited by 8
- 2026The Gray Area: Characterizing Moderator Disagreement on Redditcited by 3
- 2025Adapting to Evolving Adversaries with Regularized Continual Robust Trainingcited by 2
- 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in Englishcited by 2
- 2025HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigationcited by 1
- 2026When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platformscited by 1
- 2025Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitchcited by 0
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensionscited by 0
- 2026Beyond Effectiveness: A Multi-Criteria Framework for Comparing Practical Socio-Technical Interventionscited by 0
- 2026Beyond Effectiveness: A Multi-Criteria Framework for Comparing Practical Socio-Technical Interventions21 Aug 2026
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions18 Apr 2026
- 2026When Rules Fall Short: Agent-Driven Discovery of Emerging Content Issues in Short Video Platforms14 Jan 2026
- 2026The Gray Area: Characterizing Moderator Disagreement on Reddit4 Jan 2026
- 2025HatePRISM: Policies, Platforms, and Research Integration. Advancing NLP for Hate Speech Proactive Mitigation6 Jul 2025
- 2025Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch9 Jun 2025
- 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English11 Apr 2025
- 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations3 Mar 2025
- 2025Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey3 Mar 2025
- 2025Adapting to Evolving Adversaries with Regularized Continual Robust Training6 Feb 2025
- 2024Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators6 Nov 2024