@inproceedings{moon-etal-2023-analyzing,
title = "Analyzing Norm Violations in Live-Stream Chat",
author = "Moon, Jihyung and
Lee, Dong-Ho and
Cho, Hyundong and
Jin, Woojeong and
Park, Chan and
Kim, Minwoo and
May, Jonathan and
Pujara, Jay and
Park, Sungjoon",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.55",
doi = "10.18653/v1/2023.emnlp-main.55",
pages = "852--868",
abstract = "Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35{\%}.",
}
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<abstract>Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35%.</abstract>
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%0 Conference Proceedings
%T Analyzing Norm Violations in Live-Stream Chat
%A Moon, Jihyung
%A Lee, Dong-Ho
%A Cho, Hyundong
%A Jin, Woojeong
%A Park, Chan
%A Kim, Minwoo
%A May, Jonathan
%A Pujara, Jay
%A Park, Sungjoon
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F moon-etal-2023-analyzing
%X Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to conversations on live-streaming platforms, such as Twitch and YouTube Live, as each comment is only visible for a limited time and lacks a thread structure that establishes its relationship with other comments. In this work, we share the first NLP study dedicated to detecting norm violations in conversations on live-streaming platforms. We define norm violation categories in live-stream chats and annotate 4,583 moderated comments from Twitch. We articulate several facets of live-stream data that differ from other forums, and demonstrate that existing models perform poorly in this setting. By conducting a user study, we identify the informational context humans use in live-stream moderation, and train models leveraging context to identify norm violations. Our results show that appropriate contextual information can boost moderation performance by 35%.
%R 10.18653/v1/2023.emnlp-main.55
%U https://aclanthology.org/2023.emnlp-main.55
%U https://doi.org/10.18653/v1/2023.emnlp-main.55
%P 852-868
Markdown (Informal)
[Analyzing Norm Violations in Live-Stream Chat](https://aclanthology.org/2023.emnlp-main.55) (Moon et al., EMNLP 2023)
ACL
- Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin, Chan Park, Minwoo Kim, Jonathan May, Jay Pujara, and Sungjoon Park. 2023. Analyzing Norm Violations in Live-Stream Chat. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 852–868, Singapore. Association for Computational Linguistics.