Offensive Language Detection on Video Live Streaming Chat

Zhiwei Gao, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki


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.
Anthology ID:
2020.coling-main.175
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1936–1940
Language:
URL:
https://aclanthology.org/2020.coling-main.175
DOI:
10.18653/v1/2020.coling-main.175
Bibkey:
Cite (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.
Cite (Informal):
Offensive Language Detection on Video Live Streaming Chat (Gao et al., COLING 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.coling-main.175.pdf
Data
OLID