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An inside look into the complexity of box-office revenue prediction in China

Author

Listed:
  • Jia Xiao
  • Xin Li
  • Shanzhi Chen
  • Xuhui Zhao
  • Meng Xu

Abstract

In this article, we discuss various elements contributing to exerting influence on box office in China, which are divided into internal and external factors. Since these factors could merely be quantified by online data sources partially or inaccurately, we propose that relativity analysis is more reasonable than precise revenue prediction. Trailer is selected as the combination of movie content and online behavior prior to releasing. Indexes from seven mainstream video websites are retrieved by the designed big data system which is integrated with the Internet of things technology. Correlation coefficients of different time periods are calculated. We apply multiple linear regression with stepwise method in modeling and prove that watching counts of 1 week before releasing on Youku is the barometer of market performance, especially the first week revenues. We also manifest the power of influential users through constructing Sina Weibo acquisition and analysis system.

Suggested Citation

  • Jia Xiao & Xin Li & Shanzhi Chen & Xuhui Zhao & Meng Xu, 2017. "An inside look into the complexity of box-office revenue prediction in China," International Journal of Distributed Sensor Networks, , vol. 13(1), pages 15501477166, January.
  • Handle: RePEc:sae:intdis:v:13:y:2017:i:1:p:1550147716684842
    DOI: 10.1177/1550147716684842
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    References listed on IDEAS

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    1. 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.
    2. Max Wasserman & Satyam Mukherjee & Konner Scott & Xiao Han T. Zeng & Filippo Radicchi & Luís A. N. Amaral, 2015. "Correlations between user voting data, budget, and box office for films in the internet movie database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(4), pages 858-868, April.
    3. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    4. Thorsten Hennig-Thurau & Mark Houston & Gianfranco Walsh, 2007. "Determinants of motion picture box office and profitability: an interrelationship approach," Review of Managerial Science, Springer, vol. 1(1), pages 65-92, April.
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