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ModSandbox: Facilitating Online Community Moderation Through Error Prediction and Improvement of Automated Rules

Title
ModSandbox: Facilitating Online Community Moderation Through Error Prediction and Improvement of Automated Rules
Author(s)
Song, Jean YoungLee, SangwookLee, JisooKim, MinaKim, Juho
Issued Date
2023-04-26
Citation
ACM Conference on Human Factors in Computing Systems, pp.1 - 20
Type
Conference Paper
ISBN
9781450394215
Abstract
Despite the common use of rule-based tools for online content moderation, human moderators still spend a lot of time monitoring them to ensure they work as intended. Based on surveys and interviews with Reddit moderators who use AutoModerator, we identified the main challenges in reducing false positives and false negatives of automated rules: not being able to estimate the actual effect of a rule in advance and having difficulty figuring out how the rules should be updated. To address these issues, we built ModSandbox, a novel virtual sandbox system that detects possible false positives and false negatives of a rule and visualizes which part of the rule is causing issues. We conducted a comparative, between-subject study with online content moderators to evaluate the effect of ModSandbox in improving automated rules. Results show that ModSandbox can support quickly finding possible false positives and false negatives of automated rules and guide moderators to improve them to reduce future errors. © 2023 ACM.
URI
http://hdl.handle.net/20.500.11750/46770
DOI
10.1145/3544548.3581057
Publisher
Association for Computing Machinery
Related Researcher
  • 송진영 Song, Jean Young
  • Research Interests 인간-기계 상호작용; 인간-AI 상호작용; 크라우드소싱; 인공지능 데이터셋; 컴퓨터비전; Human-Computer Interaction; Artificial Intelligence; Human-AI Collaboration; Crowdsourcing; Computer Vision
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Appears in Collections:
Department of Electrical Engineering and Computer Science DGIST Intelligence Augmentation Group 2. Conference Papers

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