This heuristic process is costly because manual inspection requires tremendous time and effort. Most streamers and channel managers (who are now in the order of millions) manually spot informative moments and edit them. Epic moments represent “enjoyable” moments , whereas highlights are “informative” in nature .ĭespite the potential use of epic moments, the research community and industry do not have a systematic method to identify them from hours of seemingly mundane user-generated video streams. Epic moments are similar to video highlights in that they are both short summaries of long videos, yet the two are defined differently.
Short clips can also be used for promote the streamer’s channel across other web platforms, such as Reddit and YouTube. These epic moments function as an enjoyable bite-sized summary of long video content, generated typically from several hours of content. One such strategy is to create a shorter version of a minute-sized streaming video as epic moments that are well-condensed short clips.
As a result, streamers need to employ various strategies to draw new viewers in the competitive environment and keep their current viewers engaged. The increasing amount of live streaming content and broadcasters provides valuable opportunities as a democratic media platform introduces challenges in content promotion and searching. Twitch, as of June 2021, hosts 9.3 million monthly broadcasters and 2.9 million concurrent viewers . Platforms like Twitch host a wide range of content creators ordinary Internet users can broadcast their gameplays and hobbies alongside experts. For example, fandom networks have become a widespread social phenomenon, and interesting live-streamed content can be re-packaged through various channels. The combination of high interactivity and engaged audiences offers new opportunities for broadcasters. Unlike traditional media channels, modern live streaming platforms enable viewers to interact with one another and on-air broadcasters through live chats. Live streaming has emerged as a central media consumption behavior. We discuss implications of the collective decision-driven extraction in identifying diverse epic moments in a scalable way. Our user study further demonstrates that the proposed automatic model performs comparably to expert suggestions. 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). 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 current study identifies enjoyable moments in user-generated live video content by examining the audiences’ collective evaluation of its epicness. However, this process is manual and costly due to the length of online live streaming events. To retain existing viewers and attract newcomers, streamers and fans often create a well-condensed summary of the streamed content. The surging popularity of live streaming platforms has created a competitive environment. Live streaming services enable the audience to interact with one another and the streamer over live content.