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

Márton Mestyán, Taha Yasseri*, János Kertész

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    179 Citations (Scopus)
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    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.

    Original languageEnglish
    Article numbere71226
    Pages (from-to)1-8
    JournalPloS one
    Issue number8
    Publication statusPublished - 21 Aug 2013
    MoE publication typeA1 Journal article-refereed


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