mlsanityData to 7 Oct 2026

A Framework of Severity for Harmful Content Online

arXiv 2108.04401 ↗First posted 10 Aug 2021cs.HCcited by 22

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Abstract (arXiv)

The proliferation of harmful content on online social media platforms has necessitated empirical understandings of experiences of harm online and the development of practices for harm mitigation. Both understandings of harm and approaches to mitigating that harm, often through content moderation, have implicitly embedded frameworks of prioritization - what forms of harm should be researched, how policy on harmful content should be implemented, and how harmful content should be moderated. To aid efforts of better understanding the variety of online harms, how they relate to one another, and how to prioritize harms relevant to research, policy, and practice, we present a theoretical framework of severity for harmful online content. By employing a grounded theory approach, we developed a framework of severity based on interviews and card-sorting activities conducted with 52 participants over the course of ten months. Through our analysis, we identified four Types of Harm (physical, emotional, relational, and financial) and eight Dimensions along which the severity of harm can be understood (perspectives, intent, agency, experience, scale, urgency, vulnerability, sphere). We describe how our framework can be applied to both research and policy settings towards deeper understandings of specific forms of harm (e.g., harassment) and prioritization frameworks when implementing policies encompassing many forms of harm.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Aug 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 9; 2025: 5; 2026: 4.

22 papers.

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  1. 2022Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderationcited by 66
  2. 2025Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?cited by 49
  3. 2022DISARM: Detecting the Victims Targeted by Harmful Memescited by 20
  4. 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platformcited by 19
  5. 2024Moderator: Moderating Text-to-Image Diffusion Models through Fine-grained Context-based Policiescited by 15
  6. 2024Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicitiescited by 13
  7. 2024Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotatorscited by 7
  8. 2024DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorshipcited by 6
  9. 2023Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attackscited by 5
  10. 2024A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speechcited by 4
  11. 2024Sensitive Content Classification in Social Media: A Holistic Resource and Evaluationcited by 4
  12. 2023Towards Detecting Harmful Agendas in News Articlescited by 3
10 more
  1. 2025Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directionscited by 3
  2. 2025A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in Englishcited by 2
  3. 2026From Plausible to Causal: Counterfactual Semantics for Policy Evaluation in Simulated Online Communitiescited by 2
  4. 2024A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Healthcited by 1
  5. 2024Recourse for reclamation: Chatting with generative language modelscited by 0
  6. 2024The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Contentcited by 0
  7. 2025MetaHarm: Harmful YouTube Video Dataset Annotated by Domain Experts, GPT-4-Turbo, and Crowdworkerscited by 0
  8. 2026Mod-Guide: An LLM-based Content Moderation Feedback System to Address Insensitive Speech toward Indigenous Ethnic and Religious Minority Communitiescited by 0
  9. 2026Physiological Noise Augmentation Improves Non-Invasive Brain-to-Speechcited by 0
  10. 2026VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformationcited by 0