Abstract
Given the growing popularity of online games and eSports, the young generation is increasingly enjoying its video live streaming service. Offensive conversations often appear against the streamer or audience in the streaming channel’s chatroom. This research aims to detect offensive language appearing in video live streaming chats. Focusing on Twitch, the most popular live streaming platform, we created a dataset for the task of detecting offensive language. We collected chat posts across four popular game titles with genre diversity (i.e., competitive, violent, peaceful). To make use of the similarity in offensive languages among social media, we adopt the state-of-the-art models trained over the offensive language on Twitter to our Twitch data (i.e., transfer learning). Our results show that transfer from social media to live streaming is effective. However, the similarity measures we proposed show less correlation on the transferability prediction.
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Acknowledgements
This study was supported in part by JSPS KAKENHI Grant Number JP19K20279 and Health and Labor Sciences Research Grant Number H30-shinkougyousei-shitei-004.
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Gao, Z., Yada, S., Wakamiya, S., Aramaki, E. (2021). A Preliminary Analysis of Offensive Language Detection Transferability from Social Media to Video Live Streaming Platforms. In: Yada, K., et al. Advances in Artificial Intelligence. JSAI 2020. Advances in Intelligent Systems and Computing, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-73113-7_11
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DOI: https://doi.org/10.1007/978-3-030-73113-7_11
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