• DocumentCode
    2191499
  • Title

    Catching a Viral Video

  • Author

    Broxton, Tom ; Interian, Yannet ; Vaver, Jon ; Wattenhofer, Mirjam

  • fYear
    2010
  • fDate
    13-13 Dec. 2010
  • Firstpage
    296
  • Lastpage
    304
  • Abstract
    The sharing and re-sharing of videos on social sites, blogs e-mail, and other means has given rise to the phenomenon of viral videos - videos that become popular through internet sharing. In this paper we seek to better understand viral videos on You Tube by analyzing sharing and its relationship to video popularity using 1.5 million You Tube videos. The social ness of a video is quantified by classifying the referrer sources for video views as social (e.g. an emailed link) or non-social (e.g. a link from related videos). By segmenting videos according to their fraction of social views, we find that highly social videos behave differently than less social videos. For example, the highly social videos rise to, and fall from, their peak popularity more quickly than less social videos. We also find that not all highly social videos become popular, and not all popular videos are highly social. And, despite their ability to generate large volumes of views over a short period of time, only 21% of the most popular videos (in terms of 30-day views) can be classified as viral. The observations made here lay the ground work for future work related to the creation of classification and predictive models for online videos.
  • Keywords
    Internet; social networking (online); YouTube; social sites; viral video; YouTube; viral videos;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
  • Conference_Location
    Sydney, NSW
  • Print_ISBN
    978-1-4244-9244-2
  • Electronic_ISBN
    978-0-7695-4257-7
  • Type

    conf

  • DOI
    10.1109/ICDMW.2010.160
  • Filename
    5693313