Early prediction of movie box office success based on Wikipedia activity big data.
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...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Public Library of Science
2013
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| _version_ | 1826260322516729856 |
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| author | Mestyán, M Yasseri, T Kertész, J |
| author_facet | Mestyán, M Yasseri, T Kertész, J |
| author_sort | Mestyán, M |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T19:03:47Z |
| format | Journal article |
| id | oxford-uuid:146fbe08-f42c-4e03-9a04-5888002ca419 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:03:47Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:146fbe08-f42c-4e03-9a04-5888002ca4192022-03-26T10:19:55ZEarly prediction of movie box office success based on Wikipedia activity big data.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:146fbe08-f42c-4e03-9a04-5888002ca419EnglishSymplectic Elements at OxfordPublic Library of Science2013Mestyán, MYasseri, TKertész, JUse 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. |
| spellingShingle | Mestyán, M Yasseri, T Kertész, J Early prediction of movie box office success based on Wikipedia activity big data. |
| title | Early prediction of movie box office success based on Wikipedia activity big data. |
| title_full | Early prediction of movie box office success based on Wikipedia activity big data. |
| title_fullStr | Early prediction of movie box office success based on Wikipedia activity big data. |
| title_full_unstemmed | Early prediction of movie box office success based on Wikipedia activity big data. |
| title_short | Early prediction of movie box office success based on Wikipedia activity big data. |
| title_sort | early prediction of movie box office success based on wikipedia activity big data |
| work_keys_str_mv | AT mestyanm earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata AT yasserit earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata AT kerteszj earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata |