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

Title of the Paper

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

Paper Information

• Domain: Content moderation policies of online platforms
• Keywords: content moderation, dataset, qualitative analysis, quantitative analysis, community guidelines, platform rules, user-generated content, copyright infringement, hate speech, misinformation

Research Background and Problems

• Problem or Challenge: Online platforms must balance user safety and freedom of speech, which involves complex content moderation policy issues. Existing research lacks a systematic comparative understanding of content moderation policies across different platforms and topics. Additionally, many content moderation policies are complex in structure and scattered across multiple webpages, making it difficult for users to access clear policy information.
• Significance: With the global influence of the internet on social discourse, content moderation policies impact user behavior and shape societal narratives, forming a core issue in platform operations. Content moderation involves legal compliance and social responsibility, but in many countries, this field lacks clear legal frameworks, leading to inconsistencies and potential biases in policies.
• Research Motivation and Related Work: Previous studies have focused on the content moderation rules of individual platforms or limited explorations of user behavior, with few comparative studies across platforms and topics. This study aims to provide foundational data for future policy research and investigate differences in policy structures and enforcement.

Solution

• Research Methods:
  1. Develop a custom web crawler to collect content moderation policy texts from 43 major online platforms.
  2. Create a unified annotation scheme (codebook) for three major topics (copyright infringement, hate speech, and misinformation) to capture key policy components.
  3. Conduct mixed qualitative and quantitative analyses of policy texts to uncover similarities and differences across platforms and topics.
• Innovations:
  1. Developed an open-source policy collection pipeline capable of continuously updating the dataset.
  2. Designed a content policy annotation framework focused on the user perspective.
  3. Provided a fully annotated open-source policy dataset (OCMP-43) to support further in-depth research.
• Implementation Steps:
  1. Policy Collection: Use web crawlers to locate and extract policy texts, addressing challenges like dynamic loading and scattered content.
  2. Annotation Analysis: Perform multi-round manual annotation and proofreading of policy texts using the codebook to facilitate cross-platform comparisons.
  3. Results Presentation: Conduct structured analyses of policies across topics and platforms, statistically examining policy components and their distribution to reveal differences in enforcement approaches.

Research Outcomes

• Specific Results:
  1. Constructed the OCMP-43 dataset containing content moderation policies from 43 platforms, covering three major topics, over 1,000 pages of annotated documentation, and tens of thousands of text annotations.
  2. Analysis revealed significant differences in policy structure and components across platforms, particularly in policy definitions, the roles of users and platforms, and appeal mechanisms.
  3. Found that copyright-related policies rely more heavily on clear legal frameworks compared to hate speech and misinformation policies, which are often driven by community values.
• Advantages: Provides the first large-scale comparative study of cross-platform policies, offering systematic insights into policy structures, legal foundations, and user-platform interaction models.
• Experimental or Evaluation Results:
  • Platforms showed significant differences in the completeness of policy components, with only 39.5% of platforms covering all critical aspects of content moderation policies.
  • Users often lack effective appeal channels under misinformation and hate speech rules, whereas copyright infringement policies feature more comprehensive appeal processes.
  • Policies lacked specific definitions and relied more on ambiguous examples, posing challenges for user understanding and enforcement.
• Limitations and Future Directions:
  1. The dataset only covers English-language platforms, excluding non-English contexts.
  2. Policy crawling and annotation involve noise and redundancy; future efforts could enhance automation to improve efficiency.
  3. Future research should explore the actual enforcement of policies to address potential gaps between policies and practices.
  4. The dataset could be expanded to other topics (e.g., generative AI content) or used to dynamically track policy evolution.

Conclusion

By constructing the OCMP-43 dataset and conducting large-scale cross-platform analyses, this study provides a systematic framework and data support for research on platform content moderation policies. The study highlights the diversity and existing challenges in online platform content moderation policies, offering guidance for policy improvement and user research.