Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

By Márton Mestyán, Taha Yasseri, János Kertész. PLoS ONE, 2012

294 citations recorded by the source corpus.

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.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/89cc64f1ef4cd126d19e8607c28272f07d602929

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Citations in the corpus

Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data (2015, methodology): “Predictive models of box office revenue for new films have been built using Wikipedia activity [4], internet search term frequency [5] and Twitter word of mouth [6].”
Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective (2016, unknown): “Mestyán et al. (2013) found that the analysis and measurement of the activity level of editors and viewers of the corresponding entry to a movie in Wikipedia can predict the popularity of that movie much before its release.”
Style in the Age of Instagram: Predicting Success within the Fashion Industry using Social Media (2015, unknown): “For example, Asur and Huber-man [4, 3] used Twitter data to predict the box-ofﬁce performance of newly-released blockbuster movies, and later Mesty´an et al. [35] improved these results using data from Wikipedia.”