Title |
Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data
|
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Published in |
PLOS ONE, August 2013
|
DOI | 10.1371/journal.pone.0071226 |
Pubmed ID | |
Authors |
Márton Mestyán, Taha Yasseri, János Kertész |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 19 | 16% |
United Kingdom | 18 | 16% |
Italy | 5 | 4% |
Netherlands | 4 | 3% |
France | 4 | 3% |
Canada | 3 | 3% |
India | 3 | 3% |
Spain | 3 | 3% |
Austria | 2 | 2% |
Other | 20 | 17% |
Unknown | 35 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 88 | 76% |
Scientists | 25 | 22% |
Science communicators (journalists, bloggers, editors) | 3 | 3% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 1% |
Germany | 3 | <1% |
United Kingdom | 2 | <1% |
Netherlands | 1 | <1% |
France | 1 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Brazil | 1 | <1% |
Switzerland | 1 | <1% |
Other | 4 | 1% |
Unknown | 308 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 74 | 23% |
Student > Master | 73 | 22% |
Researcher | 43 | 13% |
Student > Bachelor | 32 | 10% |
Professor | 13 | 4% |
Other | 46 | 14% |
Unknown | 46 | 14% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 92 | 28% |
Social Sciences | 46 | 14% |
Business, Management and Accounting | 38 | 12% |
Engineering | 22 | 7% |
Economics, Econometrics and Finance | 14 | 4% |
Other | 56 | 17% |
Unknown | 59 | 18% |