Predicting Box Office from the Screenplay: An Empirical Model

Empirical studies of the determinants of box office revenues have mostlyfocused on post-production factors, i.e., ones known after the film has been completed and/or released. Relatively fewstudies have considered pre-production factors, i.e., ones known before a decision has been made togreenlight a film project. The current study directly addresses this gap in theliterature. Specifically, we develop and test a relatively parsimonious,pre-production model to predict the opening weekend box office of 170US-produced, English-language, feature films released in the years 2010 and2011. Chief among the pre-production factors that we consider are those derivedfrom the textual and content analysis of the screenplays of these films. Themost important of these is determined through the application of network textanalysis-a method for rendering a text as a map or network of interconnectedconcepts. As predicted, we find that the size of the main component of ascreenplay's text network strongly predicts the completed film's openingweekend box office. The research draws upon methods common to wide range ofacademic interests including business strategy and media economics, content andtext analysis, social network analysis, film studies, socio- and computationallinguistics, and morphology.

About the Presenters

SusanSmith is Associate Professor in the AUS Department of Mass Communication. StarlingDavid Hunter III is Associate Professor at Carnegie Mellon University'sTepper School of Business in Doha, Qatar.  

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