Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data
|Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data|
|Author(s)||Márton Mestyán, Taha Yasseri, János Kertész|
|Published in||Unpublished work|
|Keyword(s)||prediction, big data|
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Use of socially generated "big data" to access information about collective states of the minds in human societies becomes a new paradigm in the emerging field of computational social science. One of the natural application of this would be prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging between "real time monitoring" and "early predicting" remains as a big challenge. Here, we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie could be predicted well in advance by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.
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