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...

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Autores principales: Márton Mestyán, Taha Yasseri, János Kertész
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Publicado: Public Library of Science (PLoS) 2013
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Acceso en línea:https://doaj.org/article/cd37cbf5cadb43a686e840404d7c5eff
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spelling oai:doaj.org-article:cd37cbf5cadb43a686e840404d7c5eff2021-11-18T08:58:38ZEarly prediction of movie box office success based on Wikipedia activity big data.1932-620310.1371/journal.pone.0071226https://doaj.org/article/cd37cbf5cadb43a686e840404d7c5eff2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23990938/?tool=EBIhttps://doaj.org/toc/1932-6203Use 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.Márton MestyánTaha YasseriJános KertészPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71226 (2013)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Márton Mestyán
Taha Yasseri
János Kertész
Early prediction of movie box office success based on Wikipedia activity big data.
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.
format article
author Márton Mestyán
Taha Yasseri
János Kertész
author_facet Márton Mestyán
Taha Yasseri
János Kertész
author_sort Márton Mestyán
title 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_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_sort early prediction of movie box office success based on wikipedia activity big data.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/cd37cbf5cadb43a686e840404d7c5eff
work_keys_str_mv AT martonmestyan earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata
AT tahayasseri earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata
AT janoskertesz earlypredictionofmovieboxofficesuccessbasedonwikipediaactivitybigdata
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