Predicting the Popularity of DanMu-enabled Videos: A Multi-factor View

  • Ming He
  • Yong Ge
  • Le Wu
  • Enhong ChenEmail author
  • Chang Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9643)


Recent years have witnessed the prosperity of a new type of real-time user-generated comment, or so-called DanMu, in many recent online video platforms. These DanMu-enabled video platforms present scrolling marquee comments overlaid directly on top of the videos by synchronizing these comments to specific playback times. In this paper, we study the prediction of video popularity in these platforms, which may benefit a lot of applications ranging from online advertising for website holders to popular video recommendation for audiences. Different from traditional online video platforms where only traditional reviews are available, these DanMus make viewers easily see other viewers’ opinions and communicate with each other in a much more direct way. Consequently, viewers are easily influenced by others’ behaviors over time, which is considered as the herding effect in social science. However, how to address the unique characteristics (i.e., the herding effect) of DanMu-enabled online videos for more accurate popularity prediction is still under-explored. To that end, in this paper, we first explore and measure the herding effect of DanMu-enabled video popularity from multiple aspects, including the popular videos, the popular DanMus and the newly updated videos. Also, we recognize that the uploaders’ influence and video quality affect the video popularity as well. Along this line, we propose a model that incorporates the herding effect, uploaders’ influence and video quality for predicting the video popularity. An effective estimation method is also proposed. Finally, experimental results on real-world data show that our proposed prediction model improves the prediction accuracy by 47.19 % compared to the baselines.


Video Quality Multiple Aspect Online Video Popular Video Video Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the National High Technology Research and Development Program of China (Grant No. 2014AA015203) and the Fundamental Research Funds for the Central Universities of China (Grant No. WK2350000001). This research was supported in part by NIH (1R21AA023975-01), NSFC (71571093, 71372188, 61572032), and National Center for International Joint Research on E-Business Information Processing (2013B01035). Truly appreciate Jinmei Lin’s help and suggestions in user experience on DanMu-enabled videos.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ming He
    • 1
    • 2
  • Yong Ge
    • 2
  • Le Wu
    • 3
  • Enhong Chen
    • 1
    Email author
  • Chang Tan
    • 4
  1. 1.University of Science and Technology of ChinaHefeiChina
  2. 2.University of North Carolina at CharlotteCharlotteUSA
  3. 3.Hefei University of TechnologyHefeiChina
  4. 4.Anhui Radio and Television Information Network CO., LTDBeijingChina

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