Abstract Video-sharing platforms such as YouTube are popular today, not only for entertainment but also for learning, e.g., for understanding lecture content or acquiring everyday skills. Some platforms, like Bilibili, a popular video-sharing platform in China, even introduced note-taking and sharing features to further foster such learning practices. While note-taking and sharing have been extensively explored in environments specifically for learning, their use around these social media platforms such as YouTube and Bilibili remains under-studied. In this paper, we present a qualitative study with 15 participants who have used the note-taking and sharing feature called BNote launched on Bilibili. Our study reveals how note-taking and sharing are used as a way to cultivate a space for learning on such a general video-sharing platform by structuring, supplementing, and substituting user-generated videos and by leveraging social forces for motivation, self-discipline, and organization for learning. We end by discussing our findings and providing design recommendations to better support a learning space on social media platforms.

Danmu (or “bullet screen”), a popular feature on video sharing platforms, plays a crucial role in facilitating knowledge sharing and learning. In recent years, danmu has drawn attention to automatic generation methods. However, existing methods mostly utilize limited content sources, such as the video itself (e.g., subtitles) and neighboring danmus, while other valuable sources remain underexplored. To this end, this article proposes a Category-Guided Note-to-Danmu conversion model (CG-NTD) by leveraging user-generated notes. The model is designed to identify unique contents within the notes and convert them into danmus while also showing the source note categories. CG-NTD classifies the notes by fusing them with subtitle and neighboring danmu features. Then, it uses a cross-attention mechanism to integrate the note’s category feature with note, subtitle, and danmu contexts to identify three keywords from the notes as the generated danmus. Using Bilibili as the research site, we implement a plugin prototype named BNoteToDanmu. Automatic and human evaluations reveal that CG-NTD outperforms BiLSTM, mT5, and BERT baselines in Precision, Recall, and F1-score metrics and generates more understandable and relevant danmus than ChatGPT. Moreover, the plugin demonstrates promising applications, such as assisting users in viewing videos, posting danmus, and recognizing high-quality notes. These findings offer insights into leveraging user creations to generate danmu to enhance its learning value on video sharing platforms.

Usually generated by ordinary users and often not particularly designed for learning, the videos on video-sharing platforms are mostly not structured enough to support learning purposes, although they are increasingly leveraged for that. Most existing studies attempt to structure the video using video summarization techniques. However, these methods focus on extracting information from within the video and aiming to consume the video itself. In this article, we design and implement BNoteHelper, a note-based video outline prototype that generates outline titles by extracting user-generated notes on Bilibili, using the BART model fine-tuned on a built dataset. As a browser plugin, BNoteHelper provides users with video overview and navigation as well as note-taking template, via two main features: outline table and navigation marker. The model and prototype are evaluated through automatic and human evaluations. The automatic evaluation reveals that, both before and after fine-tuning, the BART model outperforms T5-Pegasus in BLEU and Perplexity metrics. Also, the results from user feedback reveal that the generation outline sourced from notes is preferred by users over that sourced from video captions due to its more concise, clear, and accurate characteristics but also too general with less details and diversities sometimes. Two features of the video outline are also found to have respective advantages, especially in holistic and fine-grained aspects. Based on these results, we propose insights into designing a video summary from the user-generated creation perspective, customizing it based on video types, and strengthening the advantages of its different visual styles on video-sharing platforms.

Nowadays, using the online whiteboard to share knowledge in distributed meetings has become a common practice. Existing studies and practices have attempted to visualize attendees’ interactive activities in whiteboard tools to support the virtual team’s workspace awareness (WA). However, the impact of such visual cues on meeting success remains unclear. For this purpose, we primarily explore whether and to what extent WA cues are conducive to meeting outcome. This study applies activity theory to guide our prototype design and research analysis. A customized web-based whiteboard interface is implemented under two conditions. We conduct a study with 42 subjects in a distributed meeting scenario via a controlled experiment. Also, we analyze the system affordance via user experience. The results demonstrate that the benefits of WA cues to meeting outcome are especially embodied in goal attainment and quality of contributions, but not effectively supported in productivity and user satisfaction. Moreover, subjects report that they do not feel distracted by the system’s visual cues because they do not notice those cues most of the time and use them only when needed. Drawing upon findings from our trial work, we provide several implications for designing a collaborative knowledge-sharing environment to assist the visual support of WA in distributed meetings.

Multidisciplinary provenance helps to record the detailed processes about data derivation during multidisciplinary collaboration, which is vital to result reproducibility, trustworthiness reinforcement and other further usage. However, too detailed multidisciplinary provenance information often overwhelms user due to provenance bushiness, provenance deepness and semantic complexity. This paper presents a summarization approach to simplify provenance information based on homogeneity and semantic distance. We first specify a collaborative provenance model to records multidisciplinary collaboration processes. By analyzing the difficulties of understanding multidisciplinary provenance information, a multi-granular provenance summarization approach is given. Finally, discipline-specified provenance views are provided to meet different concerns. Experimental analysis shows the effectiveness our approach from the perspectives of conciseness.