A Trade-off-centered Framework of Content Moderation
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Abstract (arXiv)
Content moderation research typically prioritizes representing and addressing challenges for one group of stakeholders or communities in one type of context. While taking a focused approach is reasonable or even favorable for empirical case studies, it does not address how content moderation works in multiple contexts. Through a systematic literature review of 86 content moderation papers that document empirical studies, we seek to uncover patterns and tensions within past content moderation research. We find that content moderation can be characterized as a series of trade-offs around moderation actions, styles, philosophies, and values. We discuss how facilitating cooperation and preventing abuse, two key elements in Grimmelmann's definition of moderation, are inherently dialectical in practice. We close by showing how researchers, designers, and moderators can use our framework of trade-offs in their own work, and arguing that trade-offs should be of central importance in investigating and designing content moderation.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2022.
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Citing papers per year: 2023: 2; 2024: 6; 2025: 1; 2026: 2.
11 papers.
- 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
- 2024Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policiescited by 15
- 2023Watch Your Language: Investigating Content Moderation with Large Language Modelscited by 14
- 2024Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Modelscited by 13
- 2023BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biasescited by 7
- 2024Algorithmic Fairness: A Tolerance Perspectivecited by 2
- 2024ARTiST: Automated Text Simplification for Task Guidance in Augmented Realitycited by 0
- 2024Safe Guard: an LLM-agent for Real-time Voice-based Hate Speech Detection in Social Virtual Realitycited by 0
- 2025SafeGen: Embedding Ethical Safeguards in Text-to-Image Generationcited by 0
- 2026Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflictscited by 0
- 2026Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communitiescited by 0
- 2026Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communities11 Jun 2026
- 2026Power Echoes: Investigating Moderation Biases in Online Power-Asymmetric Conflicts2 Mar 2026
- 2025SafeGen: Embedding Ethical Safeguards in Text-to-Image Generation14 Dec 2025
- 2024Safe Guard: an LLM-agent for Real-time Voice-based Hate Speech Detection in Social Virtual Reality23 Sep 2024
- 2024Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policies14 Aug 2024
- 2024Algorithmic Fairness: A Tolerance Perspective26 Apr 2024
- 2024Moderating Illicit Online Image Promotion for Unsafe User-Generated Content Games Using Large Vision-Language Models27 Mar 2024
- 2024ARTiST: Automated Text Simplification for Task Guidance in Augmented Reality29 Feb 2024
- 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biases19 Jan 2024
- 2023Watch Your Language: Investigating Content Moderation with Large Language Models25 Sep 2023
- 2023BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biases23 May 2023