ModSandbox: Facilitating Online Community Moderation Through Error Prediction and Improvement of Automated Rules
Jean Y. Song, Sangwook Lee, Jisoo Lee, Mina Kim, Juho Kim
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.
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