Designing Word Filter Tools for Creator-led Comment Moderation.

Author new filters
Organize filters
Visualize caught comments.

Shagun Jhaver, Quan Ze Chen, Detlef Knauss, and Amy Zhang (2022), “Designing Word Filter Tools for Creator-led Comment Moderation,” In Proceedings of the ACM CHI Conference on Human Factors in Computing Systems (CHI 2022).


Online social platforms centered around content creators often allow comments on content, where creators can then 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 revising 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 pre-made ones, as well as organizing their filters and visualizing what comments are captured by them 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.

BibTeX citation

	author = {Jhaver, Shagun and Chen, Quan Ze and Knauss,Detlef and Zhang, Amy},
	title = {Designing Word Filter Tools for Creator-led Comment Moderation},
	year = {2022},
	publisher = {Association for Computing Machinery},
	address = {New York, NY, USA},
	url = {},
	doi = {10.1145/3491102.3517505},
	booktitle = {Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems},