Designing a Human Computation Game for Enhancing Early-Phase Movie Box Office Prediction

  • Johmphot Tantawichien
  • Hajime Mizuyama
  • Tomomi Nonaka
Part of the Translational Systems Sciences book series (TSS, volume 18)


Movie production is riddled with subjectivity and uncertainty. Each decision made can affect both quality and financial aspects of movies. Previously, various mathematical box office prediction models were proposed, but they focused at the time near the movie release, while earlier predictions would have more benefits to production team. Prediction market was suggested to have good predictability, but it still has some problems. In this study, we designed a human computation game for improving mathematical model performance in early phases which limits what information player knows about the movie at different time and introduces improved mechanics to make the game more similar to the actual movie production. After the experiments, we found that the proposed human computation game did improve mathematical prediction model performance, used in this study, but with limited working conditions. Future work should consider using more complex mathematical models, improving game design, and gathering more data for further validation.


Box office prediction Movie industry Business game 


  1. 1.
    Holbrook MB (1999) Popular appeal versus expert judgments of motion pictures. J Consum Res 26(2):144–155CrossRefGoogle Scholar
  2. 2.
    King T (2007) Does film criticism affect box office earnings? Evidence from movies released in the U.S. in 2003. J Cult Econ 31(3):171–186CrossRefGoogle Scholar
  3. 3.
    Gemser G, Van Oostrum M, Leenders MAAM (2007) The impact of film reviews on the box office performance of art house versus mainstream motion pictures. J Cult Econ 31(1):43–63CrossRefGoogle Scholar
  4. 4.
    Lash MT, Zhao K (2016) Early prediction of movie success: the who, what, and when of profitability. arXiv preprint arXiv:1506.05382v2Google Scholar
  5. 5.
    Mestryán M, Yasseri T, Kertész J (2012) Early prediction of movie box office success based on Wikipedia activity big data. arXiv preprint arXiv:1211.0970Google Scholar
  6. 6.
    Eliashberg J, Hui SK, Zhang J (2014) Assessing box office performance using movie script: a kernel-based approach. IEEE Trans Knowl Data Eng 26(11):2638–2648CrossRefGoogle Scholar
  7. 7.
    Panaligan R, Chen A (2013) Quantifying movie magic with Google search. Google, CaliforniaGoogle Scholar
  8. 8.
    Doshi L (2010) Using sentiment and social network analyses to predict opening-movie box-office success. Master thesis, Massachusetts Institute of TechnologyGoogle Scholar
  9. 9.
    Gruca TS, Berg J, Ciprino M (2003) The effect of electronic markets on forecasts of new product success. Inf Syst Front 5:95–105CrossRefGoogle Scholar
  10. 10.
    Karniouchina EV (2011) Are virtual markets efficient predictors of new product success? The case of the Hollywood stock exchange. J Prod Innov Manag 28:470–484CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Johmphot Tantawichien
    • 1
  • Hajime Mizuyama
    • 1
  • Tomomi Nonaka
    • 2
  1. 1.College of Science and EngineeringAoyama Gakuin UniversitySagamiharaJapan
  2. 2.College of Gastronomy ManagementRitsumeikan UniversityKusatsuJapan

Personalised recommendations