Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses

Court, David; Gillen, Benjamin J.; McKenzie, Jordi; Plott, Charles R.

doi:10.1007/s00199-017-1036-1

Cited by 18 publications

(7 citation statements)

References 29 publications

(14 reference statements)

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“…Hwang et al (2017) use the Korean motion picture market to create a forecasting model using the big data analysis. Court, Gillen, McKenzie, and Plott (2018) employ two information aggregation mechanisms to forecast the opening weekend box office revenues of movies. Complexity in the box office prediction for the Chinese movie market is examined in Xiao, Li, Chen, Zhao, and Xu (2017).…”

Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

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Improving productivity in the entertainment industry is a very challenging task as it heavily depends on generating attractive content for the consumers. The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can design customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry. We use the natural language processing methodology combined with econometric analysis to explore whether and to what extent emotions shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analyzing 6,174 movie scripts, we generate the emotional trajectory of each motion picture. We then combine the obtained mappings into clusters which represent groupings of consumer emotional journeys. These clusters are then plugged into an econometric model to predict overall success parameters of the movies including box office revenues, viewer satisfaction levels (captured by IMDb ratings), awards, as well as the number of viewers' and critics' reviews. We find that emotional arcs in movies can be partitioned into 6 basic shapes. The highest box offices are associated with the Man in a Hole shape which is characterized by an emotional fall followed by an emotional rise. This U-shaped emotional arc results in financially successful movies irrespective of genre and production budget. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

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Section: Related Literaturementioning

confidence: 99%

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Vecchio

Kharlamov

Parry

et al. 2020

Journal of the Operational Research Society

View full text Add to dashboard Cite

show abstract

“…Consistently with that, in political voting arena meta-predictions have been shown to produce better accuracy of election forecasts, as shown in [19] and [20]. In another setting, in [21] authors use “guess of guesses” where respondents guess the box office and are rewarded for how close their guess comes to the median of all guesses. Other research uses predictions of predictions to improve on traditional wisdom of the crowds [22, 23].…”

Section: Introductionmentioning

confidence: 60%

Whose data can we trust: How meta-predictions can be used to uncover credible respondents in survey data

Radas

Prelec

2019

PLoS ONE

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Many areas of economics use subjective data, although it had been known to present problems regarding its reliability. To improve data quality, researchers may use scoring rules that reward respondents so that it is most profitable for them to tell the truth. However, if the subjects are not well informed about the topic or if they do not pay sufficient attention, they will produce data that could not be dependably used for decision-making even though subjects gave their honest answer. In this paper we show how meta-predictions (respondents’ predictions about choices of others) can be used for identification of respondents who produce dependable data. We use purchase intention survey, a popular method to elicit early adoption forecasts for a new concept, as a test bed for our approach. We present results from three online experiments, demonstrating that corrected purchase intentions are closer to the real outcomes.

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“…This mechanism employed by the NSF is not strategyproof; reviewers are incentivized to guess what others are thinking, not to provide their honest feedback. Hence the mechanism induces a type of Keynesian "Beauty Contest" [30] where the incentives are misaligned and humans have been shown to not behave truthfully [17]. Removing the bonus may be worse, as reviewers would then be able to increase the chance of their own proposal being accepted by rating other proposals lower [46].…”

Section: Introductionmentioning

confidence: 99%

Strategyproof peer selection using randomization, partitioning, and apportionment

Aziz

Lev

Mattei

et al. 2019

Artificial Intelligence

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Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a Massive Open Online Course (MOOC) or an online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation.We make three fundamental contributions to the study of peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive Email addresses: haris.aziz@data61.csiro.au (Haris Aziz), omerlev@bgu.ac.il (Omer Lev), nsmattei@tulane.edu (Nicholas Mattei), jeff@cs.huji.ac.il (Jeffrey S. Rosenschein), toby.walsh@data61.csiro.au (Toby Walsh) This is a significantly revised and expanded version of our conference paper from AAAI 2016 [3]. This version introduces the exact version of Dollar Partition along with new proofs and a new experiment.simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.

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Two information aggregation mechanisms for predicting the opening weekend box office revenues of films: Boxoffice Prophecy and Guess of Guesses

Cited by 18 publications

References 29 publications

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Improving productivity in Hollywood with data science: Using emotional arcs of movies to drive product and service innovation in entertainment industries

Whose data can we trust: How meta-predictions can be used to uncover credible respondents in survey data

Strategyproof peer selection using randomization, partitioning, and apportionment

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