Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, Sungjoon Park
| Challenge: | Existing methods for detecting toxic language and norm violations are limited to live-streaming platforms . existing methods are less effective when applied to live streaming platforms based on a limited time frame . |
| Approach: | They propose to use contextual information to automatically moderate toxic content on live streaming platforms. |
| Outcome: | The proposed model improves on live-streaming platforms by 35%. |
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Chan Young Park, Julia Mendelsohn, Karthik Radhakrishnan, Kinjal Jain, Tushar Kanakagiri, David Jurgens, Yulia Tsvetkov
| Challenge: | Existing efforts to identify unacceptable behavior have focused on toxicity as the sole form of community norm violation. |
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| Outcome: | The proposed model improves the detection of community norm violations in local conversational and global contexts. |
Offensive Language Detection on Video Live Streaming Chat (2020.coling-main)
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| Challenge: | a prototype of a live chat room that detects offensive expressions in live streaming chats is presented . offensive expression detection on social media platforms can provide more protection for users . |
| Approach: | They propose a live chat room that detects offensive expressions in live streaming chats in real time . they used a dataset from Twitch to analyze offensive expression patterns . |
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A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat (2022.emnlp-industry)
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| Challenge: | Existing methods for toxic language detection are based on deep learning, but they are not scalable considering inference speed and computational resources. |
| Approach: | They propose a method for toxic language detection that is aware of real-world scenarios by partial stacking partial stacks that feeds initial results with low confidence to meta-classifier. |
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LLM-Human Pipeline for Cultural Grounding of Conversations (2025.naacl-long)
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| Challenge: | addressing parents by name is commonplace in the West, but it is rare in most Asian cultures. |
| Approach: | They propose a Cultural Context Schema for conversations that incorporates conversational information and cultural information such as social norms, violations, etc. |
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Silencing Empowerment, Allowing Bigotry: Auditing the Moderation of Hate Speech on Twitch (2025.acl-long)
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| Challenge: | To meet the demands of content moderation, online platforms have resorted to automated systems. |
| Approach: | They conduct an audit of Twitch’s automated moderation tool (AutoMod) to investigate its effectiveness in flagging hateful content. |
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RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
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NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation (2023.emnlp-main)
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| Challenge: | Social norms fundamentally shape interpersonal communication. |
| Approach: | They propose a human-in-the-loop pipeline to synthesize a bilingual dyadic dialogue dataset with turn-by-turn annotations of social norms for Chinese and American cultures. |
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NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly (2023.emnlp-main)
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| Challenge: | Existing methods to understand acceptable behavior have focused on a single culture and manually built datasets from non-conversational settings. |
| Approach: | They propose a framework to automatically extract culture-specific norms from multi-lingual conversations. |
| Outcome: | The proposed framework extracts culture-specific norms from multi-lingual conversations. |
PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media (2026.acl-long)
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| Challenge: | Social media are shifting towards community-governed platforms where groups define their own norms. |
| Approach: | They propose a multimodal, multilingual benchmark for detecting 13,371 rule violations across 1,989 Reddit communities . they show that bigger models and increased context provide marginal gains, and universal rules like civility and self-promotion are easier to detect. |
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ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation (2023.findings-emnlp)
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| Challenge: | toxicity detection has been largely based on social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. |
| Approach: | They propose a benchmark to detect toxicity in real-world user-AI conversations . they compare existing models with social media content to find toxicity . |
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