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Characterizing popularity dynamics of online videos

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  • Ren, Zhuo-Ming
  • Shi, Yu-Qiang
  • Liao, Hao

Abstract

Online popularity has a major impact on videos, music, news and other contexts in online systems. Characterizing online popularity dynamics is nature to explain the observed properties in terms of the already acquired popularity of each individual. In this paper, we provide a quantitative, large scale, temporal analysis of the popularity dynamics in two online video-provided websites, namely MovieLens and Netflix. The two collected data sets contain over 100 million records and even span a decade. We characterize that the popularity dynamics of online videos evolve over time, and find that the dynamics of the online video popularity can be characterized by the burst behaviors, typically occurring in the early life span of a video, and later restricting to the classic preferential popularity increase mechanism.

Suggested Citation

  • Ren, Zhuo-Ming & Shi, Yu-Qiang & Liao, Hao, 2016. "Characterizing popularity dynamics of online videos," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 236-241.
    • Handle: RePEc:eee:phsmap:v:453:y:2016:i:c:p:236-241
    DOI: 10.1016/j.physa.2016.02.019
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Zhi Li & De-qing Tan, 2017. "Two-Stage Dynamic Pricing and Advertising Strategies for Online Video Services," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-8, October.
    2. Jung, Hohyun, 2023. "Eliminating the biases of user influence and item popularity in bipartite networks: A case study of Flickr and Netflix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    3. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    4. Hou, Lei & Liu, Kecheng & Liu, Jianguo & Zhang, Runtong, 2017. "Solving the stability–accuracy–diversity dilemma of recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 415-424.
    5. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    6. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.
    7. Ren, Zhuo-Ming, 2019. "Age preference of metrics for identifying significant nodes in growing citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 325-332.

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