CS-lol: a Dataset of Viewer Comment with Scene in E-sports Live-streaming

TL;DR

This paper introduces CS-lol, a large-scale dataset linking viewer comments with game scenes in E-sports live-streams, and proposes a viewer comment retrieval task to enhance understanding of viewer interactions.

Contribution

The paper presents a new dataset, CS-lol, and defines a novel viewer comment retrieval task for E-sports live-streaming analysis.

Findings

Baseline methods show the task is challenging.

CS-lol provides a valuable resource for future research.

The dataset enables improved understanding of viewer engagement.

Abstract

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.