Finding epic moments in live content through deep learning on collective decisions
School of Computing, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
2 Data Science Group, Institute of Basic Science, Daejeon, Republic of Korea
3 School of AI Convergence, Soongsil University, Seoul, Republic of Korea
Accepted: 12 July 2021
Published online: 18 August 2021
Live streaming services enable the audience to interact with one another and the streamer over live content. The surging popularity of live streaming platforms has created a competitive environment. To retain existing viewers and attract newcomers, streamers and fans often create a well-condensed summary of the streamed content. However, this process is manual and costly due to the length of online live streaming events. The current study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness. We characterize what features “epic” moments and present a deep learning model to extract them based on analyzing two million user-recommended clips and the associated chat conversations. The evaluation shows that our data-driven approach can identify epic moments from user-generated streamed content that cover various contexts (e.g., victory, funny, awkward, embarrassing). Our user study further demonstrates that the proposed automatic model performs comparably to expert suggestions. We discuss implications of the collective decision-driven extraction in identifying diverse epic moments in a scalable way.
Key words: Epic moment / Online live streams / Crowdsourced decisions / Twitch.tv
© The Author(s) 2021
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