Journal
PLOS ONE
Volume 8, Issue 8, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0071226
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Funding
- EU's 7th Framework Program's FET-Open [238597]
- Academy of Finland
- Finnish Center of Excellence program [129670]
- TEKES (FiDiPro)
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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.
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