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Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

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  • Márton Mestyán
  • Taha Yasseri
  • János Kertész

Abstract

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.

Suggested Citation

  • Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
    • Handle: RePEc:plo:pone00:0071226
    DOI: 10.1371/journal.pone.0071226
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    1. Serguei Saavedra & Jordi Duch & Brian Uzzi, 2011. "Tracking Traders' Understanding of the Market Using e-Communication Data," PLOS ONE, Public Library of Science, vol. 6(10), pages 1-7, October.
    2. Sitabhra Sinha & S. Raghavendra, 2004. "Hollywood blockbusters and long-tailed distributions: An empirical study of the popularity of movies," Industrial Organization 0406008, University Library of Munich, Germany.
    3. Nicolas Jullien, 2012. "What We Know About Wikipedia: A Review of the Literature Analyzing the Project(s)," Post-Print hal-00857208, HAL.
    4. Tim Brody & Stevan Harnad & Leslie Carr, 2006. "Earlier Web usage statistics as predictors of later citation impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1060-1072, June.
    5. Xin Shuai & Alberto Pepe & Johan Bollen, 2012. "How the Scientific Community Reacts to Newly Submitted Preprints: Article Downloads, Twitter Mentions, and Citations," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-8, November.
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