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The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity
NICTA, Australia; University of New South Wales, Sydney, NSW, Australia.
NICTA, Alexandria, NSW, Australia .
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology.
University of Saskatchewan, Canada.
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2012 (English)In: Proc. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2012, Association for Computing Machinery (ACM), 2012, 1186-1194 p.Conference paper, Published paper (Refereed)
Abstract [en]

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity.

In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2012. 1186-1194 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-80314DOI: 10.1145/2339530.2339717ISBN: 978-1-4503-1462-6 (print)OAI: oai:DiVA.org:liu-80314DiVA: diva2:546482
Conference
KDD 2012: 18th ACM SIGKDD international conference on Knowledge discovery and data mining, Beijing, China, August 12-16
Available from: 2012-08-23 Created: 2012-08-23 Last updated: 2017-03-28

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Carlsson, Niklas

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf