Scientists devise model to predict success of films
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LONDON: Producers and actors may no more have sleepless nights before their movies are released. Oxford University
scientists have devised a mathematical model, which can be used to
predict whether films will become blockbusters or flops at the box
office up to a month before they are released. Their mathematical
algorithm allows them to predict box office revenues with an overall
accuracy of around 77%.
The study authors say this level of accuracy is higher than the best existing predictive models applied by marketing firms, which they estimate to be around 57%.
The scientists say they could predict the box office takings of six out of 312 films with 99% accuracy where the predicted value was within 1% of the real value.
Over 20 movies were predicted with 90% accuracy and 70 with an accuracy of 70% and above.
Their model is based on an analysis of the activity on Wikipedia pages about American films released in 2009 and 2010.
They examined 312 movies, taking into account the number of page views for the movie's article, the number of human editors contributing to the article, the number of edits made and the diversity of online users.
The researchers from Oxford University, the Central European University at Budapest, and Budapest University of Technology and Economics published their findings on Thursday.
Taha Yasseri from the Oxford Internet Institute at the University of Oxford, said, "These results can be of great value to marketing firms but more importantly for us, we were able to demonstrate how we can use socially generated online data to predict a lot about future human behaviour.''
"The predicting power of the Wikipedia-based model, despite its simplicity compared with Twitter, is that many of the editors of the Wikipedia pages about the movies are committed movie-goers who gather and edit relevant material well before the release date. By contrast, the mass production of tweets occurs very close to the release time, and often these can be spun by marketing agencies rather than reflecting the feelings of the public.''
The model was applied retrospectively so the researchers systematically charted the online buzz on Wikipedia around particular films and compared this with the box office takings from the first weekend after release.
The results of the comparison between the predicted opening weekend revenue, using their mathematical model, and the actual figures showed a high degree of correlation.
The more successful the film, the more accurately the researchers were able to predict box office takings.
In the study, they explain that this is possibly due to the increased amount of online data generated by films that turn out to be successes.
The model correctly forecasted the commercial success of Iron Man 2, Alice in Wonderland, Toy Story 3 and Inception. But it failed to accurately forecast the financial return on the less successful movies Never Let Me Go and Animal Kingdom.
Co-author Janos Kertesz from the Central European University of Budapest, Hungary, said, "We have demonstrated for the first time that Wikipedia edit statistics provide us with another tool to predict social events.''
Kertesz said they studied the problem of predicting the financial success of movies and concluded that, in some aspects, forecasting based on Wikipedia outperforms tweets as Wikipedia activity has a longer timescale which enables earlier predictions.
The study authors say this level of accuracy is higher than the best existing predictive models applied by marketing firms, which they estimate to be around 57%.
The scientists say they could predict the box office takings of six out of 312 films with 99% accuracy where the predicted value was within 1% of the real value.
Over 20 movies were predicted with 90% accuracy and 70 with an accuracy of 70% and above.
Their model is based on an analysis of the activity on Wikipedia pages about American films released in 2009 and 2010.
They examined 312 movies, taking into account the number of page views for the movie's article, the number of human editors contributing to the article, the number of edits made and the diversity of online users.
The researchers from Oxford University, the Central European University at Budapest, and Budapest University of Technology and Economics published their findings on Thursday.
Taha Yasseri from the Oxford Internet Institute at the University of Oxford, said, "These results can be of great value to marketing firms but more importantly for us, we were able to demonstrate how we can use socially generated online data to predict a lot about future human behaviour.''
"The predicting power of the Wikipedia-based model, despite its simplicity compared with Twitter, is that many of the editors of the Wikipedia pages about the movies are committed movie-goers who gather and edit relevant material well before the release date. By contrast, the mass production of tweets occurs very close to the release time, and often these can be spun by marketing agencies rather than reflecting the feelings of the public.''
The model was applied retrospectively so the researchers systematically charted the online buzz on Wikipedia around particular films and compared this with the box office takings from the first weekend after release.
The results of the comparison between the predicted opening weekend revenue, using their mathematical model, and the actual figures showed a high degree of correlation.
The more successful the film, the more accurately the researchers were able to predict box office takings.
In the study, they explain that this is possibly due to the increased amount of online data generated by films that turn out to be successes.
The model correctly forecasted the commercial success of Iron Man 2, Alice in Wonderland, Toy Story 3 and Inception. But it failed to accurately forecast the financial return on the less successful movies Never Let Me Go and Animal Kingdom.
Co-author Janos Kertesz from the Central European University of Budapest, Hungary, said, "We have demonstrated for the first time that Wikipedia edit statistics provide us with another tool to predict social events.''
Kertesz said they studied the problem of predicting the financial success of movies and concluded that, in some aspects, forecasting based on Wikipedia outperforms tweets as Wikipedia activity has a longer timescale which enables earlier predictions.
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