Abstract:  Online platforms increasingly rely on hybrid moderation systems that combine automated triage with professional human review, yet little research compares how these actors judge the same real content in production-linked settings. This article examines uncivil online comments as a platform-governance problem by comparing three decision-makers: registered users, a platform AI system, and professional moderators. Using a secondary dataset from a US-based publisher comment environment, we analyze real comments spanning five moderation categories: Violence, Doxxing, Hate Speech, Sexual Explicitness, and Personal Attacks. The dataset combines production-stage AI workflow dispositions, professional moderators’ final adjudications, and ex-post survey judgments from 863 registered users, yielding 8,588 user-comment decisions. Results show that users chose to allow publication of roughly two-thirds of the uncivil comments and that their decisions aligned more closely with the AI system’s publish-and-moderate versus hold-and-moderate orientation than with professional moderators’ publish-versus-block decisions. Divergence was concentrated in specific categories and items, especially Hate Speech. Users’ justifications suggest that permissive decisions often relied on threshold reasoning, rejection of toxicity, or free-speech concerns, whereas restrictive decisions more often relied on direct violation labels. Demographic and engagement-based predictors explained little variance in alignment. The findings suggest that users, AI systems, and professional moderators should not be treated as interchangeable moderation tools: they operationalize different thresholds and may be suited to different governance functions.

Keywords:  AI-assisted moderation; hybrid moderation; platform governance; uncivil comments; uncivil online communication; user-judgments

Published:   30 July 2026

Issue:   Vol 14 (2026): The Role of AI for Counter Speech: Detection, Intervention, and Risks (In Progress)