title: Early prediction of movie box office success based on Wikipedia activity big data.
abstract: 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.
authors.affiliation: Institute of Physics, Budapest University of Technology and Economics, Budapest, Hungary.
authors.forename: Márton
authors.initials: M
authors.lastname: Mestyán
authors.forename: Taha
authors.initials: T
authors.lastname: Yasseri
authors.forename: János
authors.initials: J
authors.lastname: Kertész
mesh_terms: D001519:Behavior; D003625:Data Collection; D005544:Forecasting; D006801:Humans; D020407:Internet; D016014:Linear Models; D008402:Mass Media; D015233:Models, Statistical; D009040:Motion Pictures / Q000639:trends*; D012984:Software; D013997:Time Factors