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||PLoS ONE|
|Keyword(s)||Unknown (Extra: access to information, article, audiovisual equipment, calculation, computer prediction, computer program, correlation coefficient, financial information system, information processing, information retrieval, multiple linear regression analysis, online system, United States, Behavior, Data Collection, Forecasting, Humans, Internet, Linear Models, Mass Media, Models, Statistical, Motion Pictures as Topic, Software, Time Factors)|
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Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data is a 2013 journal article written in English by Márton Mestyán, Taha Yasseri, János Kertész and published in PLoS ONE.
Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains 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 can be predicted much before its release 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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