Multimedia Tools and Applications

, Volume 75, Issue 10, pp 6035–6058 | Cite as

How YouTube videos are discovered and its impact on video views

  • Renjie Zhou
  • Samamon Khemmarat
  • Lixin Gao
  • Jian Wan
  • Jilin Zhang


As the largest video sharing site around the world, YouTube has been changing the way people entertain, gain popularity, and advertise. Discovering the major sources that drive views to a video and understanding how they impact the view growth pattern have become interesting topics for researchers as well as advertisers, media companies, or anyone who wish to have a shortcut to stardom. The work of this paper is to identify three major view sources, related video recommendation, YouTube search, and video highlight such as popular video list on YouTube homepage or video embedding on social networking sites, and examine the patterns of views from each view source. First, the impact of each view source on the view diversity and on the view share of each individual video is analyzed. It is found that while search and highlight create an effect of rich-get-richer, the related video recommendation equalizes the view distribution and helps users find niche videos. Second, the contribution of the three view sources to video popularity growth is investigated. The investigation reveals that search and related video recommendation are the two major sources that persistently drive views to a video. The view rates from recommendation and search are generally stabilized to be constant view rates. Third, the underlying factors that affect the long-term view rate from referrer videos are explored. The results indicate that the top referrer video set of a video is fairly stable and the view rate from recommendation is mainly determined by view rates of top referrer videos. Finally, whether highlight increases the view rate of a video after the duration of promotion is studied. The observations suggest that video highlight does not directly impact the view rate of a video after the event finishes. The findings presented in the paper provide several key insights into the impact and patterns of view contributions for each major source of the video views.


YouTube View sources View diversity View growth Recommendation system Referrer videos Search Highlight 



The authors are grateful to the anonymous reviewers for their valuable comments and constructive suggestions, and also to the editors for their careful checking of the publication details that improved this paper. This work was supported by NSF of Zhejiang under grant NO.LQ13F020017, LY16F020018, and NSF of China under grant NO.61300211, 61202094, 61572163, 61472112, and National Key Technology Research and Development Program of China under grant No.2014BAK14B00.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Renjie Zhou
    • 1
    • 2
  • Samamon Khemmarat
    • 3
  • Lixin Gao
    • 3
  • Jian Wan
    • 1
    • 2
  • Jilin Zhang
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityZhejiangChina
  2. 2.Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of EducationZhejiangChina
  3. 3.Department of Electrical and Computer EngineeringUniversity of MassachusettsAmherstUSA

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