# CoKnowledge: Supporting Assimilation of Time-synced Collective Knowledge in Online Science Videos

arXiv: https://arxiv.org/abs/2502.03767 · HTML: https://mlsanity.com/paper/2502.03767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition](https://mlsanity.com/paper/2509.21086/) (2025)
- ["Shall We Dig Deeper?": Designing and Evaluating Strategies for LLM Agents to Advance Knowledge Co-Construction in Asynchronous Online Discussions](https://mlsanity.com/paper/2509.23327/) (2025)
- [Designing Multi-Robot Ground Video Sensemaking with Public Safety Professionals](https://mlsanity.com/paper/2602.08882/) (2026)
- [ComVi: Context-Aware Optimized Comment Display in Video Playback](https://mlsanity.com/paper/2603.26173/) (2026)

## Abstract (arXiv)

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 a video abstract, knowledge graphs, and supplementary danmaku features to support viewers' assimilation of collective knowledge in science videos. A within-subject study (N=24) showed that CoKnowledge significantly enhanced 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 features, we presented design considerations for developing similar support tools.