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

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Database Systems for Advanced Applications (DASFAA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9643))

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Abstract

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.

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Notes

  1. 1.

    http://www.acfun.tv/.

  2. 2.

    http://www.bilibili.com/.

  3. 3.

    http://www.youtube.com/.

  4. 4.

    http://digi.163.com/14/0915/17/A66VE805001618JV.html.

  5. 5.

    Available at http://www.acfun.tv/v/ac1731008.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Ming He & Enhong Chen

  2. University of North Carolina at Charlotte, Charlotte, USA

    Ming He & Yong Ge

  3. Hefei University of Technology, Hefei, China

    Le Wu

  4. Anhui Radio and Television Information Network CO., LTD, Beijing, China

    Chang Tan

Authors
  1. Ming He
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  2. Yong Ge
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  3. Le Wu
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  4. Enhong Chen
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  5. Chang Tan
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Corresponding author

Correspondence to Enhong Chen .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology , Atlanta, Georgia, USA

    Shamkant B. Navathe

  2. University of Texas at Dallas , Richardson, Texas, USA

    Weili Wu

  3. University of Minnesota , Minneapolis, Minnesota, USA

    Shashi Shekhar

  4. Renmin University , Beijing, China

    Xiaoyong Du

  5. Fudan University , Shanghai, China

    Sean X. Wang

  6. Rutgers, The State University of New Jer , New Brunswick, New Jersey, USA

    Hui Xiong

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He, M., Ge, Y., Wu, L., Chen, E., Tan, C. (2016). Predicting the Popularity of DanMu-enabled Videos: A Multi-factor View. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_22

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  • DOI: https://doi.org/10.1007/978-3-319-32049-6_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32048-9

  • Online ISBN: 978-3-319-32049-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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