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

Human-Computer Interaction · Submitted 8 May 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We find significant structural and compositional variation in policies across topics and platforms, with some variation attributable to disparate legal groundings.

how it is stated · asserted 0.99 read the record →

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arXiv ID2405.05225 (version 1)
Pith IDXGSQTSJD, Pith's permanent record of this paper and its review
CitationArjun Nitin Bhagoji, Brennan Schaffner, Chenhao Tan, Genevieve Lakier, Grace Wang, Jacqueline Mei, Jay L. Shen, Marshini Chetty, Nick Feamster, Siyuan Cheng. ""Community Guidelines Make this the Best Party on the Internet": An In-Depth Study of Online Platforms' Content Moderation Policies." arXiv:2405.05225 (2024). https://pith.science/paper/XGSQTSJD
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,502 input tokens.

What the abstract itself states, as probabilities: claims first 0.96 · states limitation 0.28 · human participants 0.10 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.53 · compares with prior results 0.09 · mentions code or data availability 0.05.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Moderating user-generated content on online platforms is crucial for balancing user safety and freedom of speech. background 1.00 0.04 0.03 asserted 1.00
s1 Particularly in the United States, platforms are not subject to legal constraints prescribing permissible content. background 1.00 0.06 0.02 asserted 1.00
s2 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. gap 1.00 0.05 0.04 asserted 1.00
s3 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. contribution 1.00 0.92 0.08 asserted 1.00
s4 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. method 1.00 0.97 0.02 asserted 0.99
s5 We find significant structural and compositional variation in policies across topics and platforms, with some variation attributable to disparate legal groundings. result 1.00 0.96 0.71 asserted 0.99
s6 We lay the groundwork for future studies of ever-evolving content moderation policies and their impact on users. future work 1.00 0.63 0.06 asserted 0.95

What kind of paper the classifier reports this is: empirical study 0.98 · evidence from analysis of existing data 0.60.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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