@inproceedings{ou-etal-2026-rethinking,
title = "Rethinking Content Moderation via Severity-Aware Ranking",
author = "Ou, Yang and
Zhang, Jielei and
Dai, Yu and
Yang, Zian and
Yang, Run and
Li, Xiang and
Wang, Weihang and
Zhao, Xueqian and
Li, Peiyi and
Gao, Longwen",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.100/",
pages = "1738--1753",
ISBN = "979-8-89176-460-6",
abstract = "Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO{'}s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback."
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<abstract>Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO’s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback.</abstract>
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%0 Conference Proceedings
%T Rethinking Content Moderation via Severity-Aware Ranking
%A Ou, Yang
%A Zhang, Jielei
%A Dai, Yu
%A Yang, Zian
%A Yang, Run
%A Li, Xiang
%A Wang, Weihang
%A Zhao, Xueqian
%A Li, Peiyi
%A Gao, Longwen
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F ou-etal-2026-rethinking
%X Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO’s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback.
%U https://aclanthology.org/2026.emnlp-industry.100/
%P 1738-1753
Markdown (Informal)
[Rethinking Content Moderation via Severity-Aware Ranking](https://aclanthology.org/2026.emnlp-industry.100/) (Ou et al., EMNLP 2026)
ACL
- Yang Ou, Jielei Zhang, Yu Dai, Zian Yang, Run Yang, Xiang Li, Weihang Wang, Xueqian Zhao, Peiyi Li, and Longwen Gao. 2026. Rethinking Content Moderation via Severity-Aware Ranking. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 1738–1753, Budapest, Hungary. Association for Computational Linguistics.