Understanding the Users and Videos by Mining a Novel <i>Danmu</i> Dataset

Lv, Guangyi; Zhang, Kun; Wu, Le; Chen, Enhong; Xu, Tong; Liu, Qi; He, Wei

doi:10.1109/tbdata.2019.2950411

Cited by 14 publications

(3 citation statements)

References 51 publications

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“…From the perspective of video viewers, TSCs serve as a communication medium between viewers and videos, which improves the degree of viewer involvement (Lv et al, 2019). Posting TSCs helps a viewer concentrate on a video and also improves their retention of the video content and sense of fulfillment after watching it (Li et al, 2021a).…”

Section: Time-sync Comments In Online Videosmentioning

confidence: 99%

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Gao,

Yao,

Wang

et al. 2024

European Journal of Operational Research

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Section: Time-sync Comments In Online Videosmentioning

confidence: 99%

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Gao,

Yao,

Wang

et al. 2024

European Journal of Operational Research

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“…Ma and Cao [MC17] found that humor, sarcasm and happiness are three primary emotions that evoke the impulse to post danmu. Besides, existing works make further statistical analysis about danmu information [LZW∗22, HGC∗18, WSH19]. Lv et al [LZW∗22] analyzed the unique attributes of danmu, including the color, display type, emoji.…”

Section: Related Workmentioning

confidence: 99%

“…Besides, existing works make further statistical analysis about danmu information [LZW∗22, HGC∗18, WSH19]. Lv et al [LZW∗22] analyzed the unique attributes of danmu, including the color, display type, emoji. He et al [HGC∗18] found a strong herding effect in danmu posting.…”

Section: Related Workmentioning

confidence: 99%

DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos

Chen

et al. 2022

Computer Graphics Forum

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Danmu (Danmaku) is a unique social media service in online videos, especially popular in Japan and China, for viewers to write comments while watching videos. The danmu comments are overlaid on the video screen and synchronized to the associated video time, indicating viewers' thoughts of the video clip. This paper introduces an interactive visualization system to analyze danmu comments and associated viewer behaviors in a collection of videos and enable detailed exploration of one video on demand. The watching behaviors of viewers are identified by comparing video time and post time of viewers' danmu. The system supports analyzing danmu content and viewers' behaviors against both video time and post time to gain insights into viewers' online participation and perceived experience. Our evaluations, including usage scenarios and user interviews, demonstrate the effectiveness and usability of our system.

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Meaningfulness and Unit of Zipf’s Law: Evidence from Danmu Comments

Zhou

2021

Lecture Notes in Computer Science

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Understanding the Users and Videos by Mining a Novel Danmu Dataset

Cited by 14 publications

References 51 publications

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

DanmuVis: Visualizing Danmu Content Dynamics and Associated Viewer Behaviors in Online Videos

Meaningfulness and Unit of Zipf’s Law: Evidence from Danmu Comments

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