Designing Word Filter Tools for Creator-led Comment Moderation
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Abstract (arXiv)
Online social platforms centered around content creators often allow comments on content, where creators moderate the comments they receive. As creators can face overwhelming numbers of comments, with some of them harassing or hateful, platforms typically provide tools such as word filters for creators to automate aspects of moderation. From needfinding interviews with 19 creators about how they use existing tools, we found that they struggled with writing good filters as well as organizing and revisiting their filters, due to the difficulty of determining what the filters actually catch. To address these issues, we present FilterBuddy, a system that supports creators in authoring new filters or building from existing filter lists, as well as organizing their filters and visualizing what comments are captured over time. We conducted an early-stage evaluation of FilterBuddy with YouTube creators, finding that participants see FilterBuddy not just as a moderation tool, but also a means to organize their comments to better understand their audiences.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Feb 2022.
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Citing papers per year: 2024: 2; 2025: 3; 2026: 2.
7 papers.
- 2025MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governancecited by 13
- 2024DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorshipcited by 6
- 2024AI and the Future of Digital Public Squarescited by 6
- 2025Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadatacited by 0
- 2025Label Forensics: Interpreting Hard Labels in Black-Box Text Classifiercited by 0
- 2026Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Frameworkcited 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
- 2026Who Decides What Is Harmful? Content Moderation Policy Through A Multi-Agent Personalised Inference Framework2 May 2026
- 2025Label Forensics: Interpreting Hard Labels in Black-Box Text Classifier1 Dec 2025
- 2025MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance20 May 2025
- 2025Context-Aware Toxicity Detection in Multiplayer Games: Integrating Domain-Adaptive Pretraining and Match Metadata2 Apr 2025
- 2024AI and the Future of Digital Public Squares13 Dec 2024
- 2024DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship4 Nov 2024