Temporal Segmentation of Creative Live Streams

Fraser, C. Ailie; Kim, Joy O.; Shin, Hijung Valentina; Brandt, Jonathan; Dontcheva, Mira

doi:10.1145/3313831.3376437

Cited by 30 publications

(17 citation statements)

References 19 publications

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“…This is because videos are often unplanned, and there are no timetables to facilitate skipping to specific content. Consistent with prior work [16], live streams are often very long, so useful information is more likely to be scattered throughout the video, and since the stream is not edited, not all of the content may be engaging or helpful.…”

Section: 46mentioning

confidence: 60%

“…However, our problem requires context that might link to multiple points of the video. Similar to prior work that automatically segmenting live stream archives [16], we suggest developing a chat interface that is automatically organized by anchor points, . In this way, the system would automatically anchor and manage the stream content based on the chat time and natural language context, Lightweight stream archiving tools.…”

Section: Creating New Tools For Content Retrieval and Understandingmentioning

confidence: 99%

See 1 more Smart Citation

Towards Supporting Programming Education at Scale via Live Streaming

Chen

Lasecki

Dong

2021

Proc. ACM Hum.-Comput. Interact.

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Live streaming, which allows streamers to broadcast their work to live viewers, is an emerging practice for teaching and learning computer programming. Participation in live streaming is growing rapidly, despite several apparent challenges, such as a general lack of training in pedagogy among streamers and scarce signals about a stream's characteristics (e.g., difficulty, style, and usefulness) to help viewers decide what to watch. To understand why people choose to participate in live streaming for teaching or learning programming, and how they cope with both apparent and non-obvious challenges, we interviewed 14 streamers and viewers about their experience with live streaming programming. Among other results, we found that the casual and impromptu nature of live streaming makes it easier to prepare than pre-recorded videos, and viewers have the opportunity to shape the content and learning experience via real-time communication with both the streamer and each other. Nonetheless, we identified several challenges that limit the potential of live streaming as a learning medium. For example, streamers voiced privacy and harassment concerns, and existing streaming platforms do not adequately support viewer-streamer interactions, adaptive learning, and discovery and selection of streaming content. Based on these findings, we suggest specialized tools to facilitate knowledge sharing among people teaching and learning computer programming online, and we offer design recommendations that promote a healthy, safe, and engaging learning environment.

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Section: 46mentioning

confidence: 60%

Section: Creating New Tools For Content Retrieval and Understandingmentioning

confidence: 99%

Towards Supporting Programming Education at Scale via Live Streaming

Chen

Lasecki

Dong

2021

Proc. ACM Hum.-Comput. Interact.

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“…To help video consumers skim and navigate to content of interest, prior work introduced approaches to navigate videos based on transcripts [33,54,55], high-level chapters and scenes [13,19,34,54,56,80,84], or key objects and concepts [12,44,59]. While transcripts help users efficiently search for words used in the video [33,54,55], they can be difficult to skim as they are often long, unstructured, and contain disfluencies present in speech [56].…”

Section: Video Navigation Interaction Techniquesmentioning

confidence: 99%

AVscript: Accessible Video Editing with Audio-Visual Scripts

Huh,

Yang,

Peng

et al. 2023

Preprint

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“…Another category of systems aims to summarize the contents of streams to reduce information overload. For instance, Fraser et al developed algorithms to split livestreams of people using creative tools (e.g., image editors) into meaningful segments that can be used to create shorter clips or tables of contents [22]. Kobs et al fne-tuned sentiment analysis for stream text chats, which can help streamers feel more engaged with real-time audience reactions [30].…”

Section: Hci Systems To Support Livestreamingmentioning

confidence: 99%