上海科技大学知识管理系统

Danmaku, a system of scene-aligned, time-synced, floating comments, can augment video content to create “collective knowledge”. However, its chaotic nature often hinders viewers from effectively assimilating the collective knowledge, especially in knowledge-intensive science videos. With a formative study, we examined viewers' practices for processing collective knowledge and the specific barriers they encountered. Building on these insights, we designed a processing pipeline to filter, classify, and cluster danmaku, leading to the development of CoKnowledge—a tool incorporating video abstracts, knowledge graphs, and supplementary danmaku features. Through a within-subject study (N=24), CoKnowledge could significantly enhance participants’ comprehension and recall of collective knowledge compared to a baseline with unprocessed live comments. Based on our analysis of user interaction patterns and feedback on design elements, we presented design considerations for developing similar support tools.