@inproceedings{gao-etal-2020-offensive,
title = "Offensive Language Detection on Video Live Streaming Chat",
author = "Gao, Zhiwei and
Yada, Shuntaro and
Wakamiya, Shoko and
Aramaki, Eiji",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.175/",
doi = "10.18653/v1/2020.coling-main.175",
pages = "1936--1940",
abstract = "This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction."
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%0 Conference Proceedings
%T Offensive Language Detection on Video Live Streaming Chat
%A Gao, Zhiwei
%A Yada, Shuntaro
%A Wakamiya, Shoko
%A Aramaki, Eiji
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F gao-etal-2020-offensive
%X This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.
%R 10.18653/v1/2020.coling-main.175
%U https://aclanthology.org/2020.coling-main.175/
%U https://doi.org/10.18653/v1/2020.coling-main.175
%P 1936-1940
Markdown (Informal)
[Offensive Language Detection on Video Live Streaming Chat](https://aclanthology.org/2020.coling-main.175/) (Gao et al., COLING 2020)
ACL
- Zhiwei Gao, Shuntaro Yada, Shoko Wakamiya, and Eiji Aramaki. 2020. Offensive Language Detection on Video Live Streaming Chat. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1936–1940, Barcelona, Spain (Online). International Committee on Computational Linguistics.