CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming
This addresses the need for resources to study real-time viewer interactions in e-sports live-streaming, though it is incremental as it primarily introduces a new dataset and task.
The authors tackled the problem of analyzing viewer comments in e-sports live-streaming by developing CS-lol, a large-scale dataset pairing comments with game scene descriptions, and proposed a viewer comment retrieval task, which baseline methods showed to be challenging.
Billions of live-streaming viewers share their opinions on scenes they are watching in real-time and interact with the event, commentators as well as other viewers via text comments. Thus, there is necessary to explore viewers' comments with scenes in E-sport live-streaming events. In this paper, we developed CS-lol, a new large-scale dataset containing comments from viewers paired with descriptions of game scenes in E-sports live-streaming. Moreover, we propose a task, namely viewer comment retrieval, to retrieve the viewer comments for the scene of the live-streaming event. Results on a series of baseline retrieval methods derived from typical IR evaluation methods show our task as a challenging task. Finally, we release CS-lol and baseline implementation to the research community as a resource.