Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data
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{"title"=>"Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data", "type"=>"journal", "authors"=>[{"first_name"=>"Márton", "last_name"=>"Mestyán", "scopus_author_id"=>"55830068600"}, {"first_name"=>"Taha", "last_name"=>"Yasseri", "scopus_author_id"=>"26531952500"}, {"first_name"=>"János", "last_name"=>"Kertész", "scopus_author_id"=>"35473453300"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84882719407", "doi"=>"10.1371/journal.pone.0071226", "sgr"=>"84882719407", "arxiv"=>"1211.0970", "isbn"=>"1932-6203", "pmid"=>"23990938", "issn"=>"19326203", "pui"=>"369619800"}, "id"=>"202585e2-409f-346d-be8f-81adeeccd4cd", "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.", "link"=>"http://www.mendeley.com/research/early-prediction-movie-box-office-success-based-wikipedia-activity-big-data", "reader_count"=>222, "reader_count_by_academic_status"=>{"Unspecified"=>6, "Professor > Associate Professor"=>11, "Researcher"=>32, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>61, "Student > Postgraduate"=>7, "Student > Master"=>53, "Other"=>3, "Student > Bachelor"=>24, "Lecturer"=>6, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>6, "Professor > Associate Professor"=>11, "Researcher"=>32, "Student > Doctoral Student"=>10, "Student > Ph. D. Student"=>61, "Student > Postgraduate"=>7, "Student > Master"=>53, "Other"=>3, "Student > Bachelor"=>24, "Lecturer"=>6, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>12, "Agricultural and Biological Sciences"=>4, "Arts and Humanities"=>5, "Philosophy"=>1, "Business, Management and Accounting"=>26, "Chemistry"=>2, "Computer Science"=>80, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>3, "Economics, Econometrics and Finance"=>10, "Engineering"=>16, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>1, "Mathematics"=>6, "Medicine and Dentistry"=>1, "Design"=>1, "Physics and Astronomy"=>6, "Psychology"=>6, "Social Sciences"=>40}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>40}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>6}, "Psychology"=>{"Psychology"=>6}, "Mathematics"=>{"Mathematics"=>6}, "Unspecified"=>{"Unspecified"=>12}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>5}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>16}, "Chemistry"=>{"Chemistry"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>3}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>10}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>80}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>26}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Romania"=>1, "United States"=>4, "United Kingdom"=>2, "Portugal"=>1, "Switzerland"=>1, "Netherlands"=>1, "Korea (South)"=>1, "Brazil"=>1, "Italy"=>1, "Israel"=>1, "Slovakia"=>1, "Australia"=>1, "France"=>1, "Germany"=>3}, "group_count"=>16}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1178406"], "description"=>"<p>A: Time of creation of the corresponding article in Wikipedia, shown in days of <i>movie time</i> ( is the release time), B: Release weekend box office revenue in the U. S., in USD C: <i>number of theaters</i> that screened the movie on the first weekend, D: Accumulated <i>number of views</i>, and E: <i>users</i>, F: <i>edits</i>, G: <i>rigor</i> for the Wikipedia page up to days after release.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "variables", "movies"], "article_id"=>779326, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Histograms_of_different_variables_for_our_sample_of_movies_from_2010_/779326", "title"=>"Histograms of different variables for our sample of movies from 2010.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178408"], "description"=>"<p>The shorthands , , , , and denote the <i>number of views</i>, the <i>number of users</i>, the <i>rigor</i>, the <i>number of edits</i>, and the <i>number of theaters</i>, respectively. Time is measured in movie time. <i>Inset</i>: magnified detail of the main panel, showing the Pearson correlation around the day of release. Dashed horizontal line shows the correlation for <i>the number of theaters</i>.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "pearson"], "article_id"=>779328, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Temporal_evolution_of_the_Pearson_correlation_of_the_box_office_revenue_with_different_predictors_/779328", "title"=>"Temporal evolution of , the Pearson correlation of the box office revenue with different predictors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178409"], "description"=>"<p>The shorthands , , , , and denote the <i>number of views</i>, the <i>number of users</i>, the <i>rigor</i>, the <i>number of edits</i>, and the <i>number of theaters</i>, respectively. The coefficient of determination was calculated using 10-fold cross-validation (see the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071226#s4\" target=\"_blank\">Methods</a> section). The dashed gray line shows the coefficient of determination for linear regression solely based on the <i>number of theaters</i>.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "multivariate", "linear", "regression", "fed"], "article_id"=>779329, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coefficient_of_determination_of_the_multivariate_linear_regression_model_fed_by_different_set_of_input_variables_/779329", "title"=>"Coefficient of determination of the multivariate linear regression model fed by different set of input variables.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178410"], "description"=>"<p>Same sample of 24 movies is considered as both training and test set. The coefficient of determination obtained with the Twitter-based method is 0.98 at the night of the release (day 0 in movie time).</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "twitter-based", "asur", "huberman"], "article_id"=>779330, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_results_with_the_Twitter_based_prediction_in_Asur_and_Huberman_work_27_/779330", "title"=>"Comparison of the results with the Twitter-based prediction in Asur and Huberman work [27].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178412"], "description"=>"<p>Green dots are representing the smaller sample of 24 movies common in Twitter and Wikipedia studies, and black dots are movies from the 2010 sample of 312 movies. Note that negative predicted revenues for some of the very unpopular movies could not be shown in the logarithmic scale.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "wikipedia"], "article_id"=>779332, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_First_weekend_box_office_revenue_in_the_U_S_against_its_predicted_value_by_the_Wikipedia_model_at_days_/779332", "title"=>"First weekend box office revenue in the U. S. against its predicted value by the Wikipedia model at days.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178413"], "description"=>"<p>Each tick on the axis represents a modification of the page. Different tick styles refer to different users.</p>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "variables", "characterizing", "wikipedia", "editors"], "article_id"=>779333, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_different_variables_characterizing_the_activity_of_Wikipedia_editors_on_an_article_/779333", "title"=>"Illustration of different variables characterizing the activity of Wikipedia editors on an article.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-08-21 02:15:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/1178415", "https://ndownloader.figshare.com/files/1178416"], "description"=>"<div><p>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.</p></div>", "links"=>[], "tags"=>["Computer applications", "Web-based applications", "Applied mathematics", "Complex systems", "statistics", "Interdisciplinary physics", "Statistical mechanics", "communications", "Media studies", "Sociology", "Computational sociology", "culture", "Social research", "wikipedia"], "article_id"=>779335, "categories"=>["Information And Computing Sciences", "Mathematics", "Physics", "Sociology"], "users"=>["Márton Mestyán", "Taha Yasseri", "János Kertész"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0071226.s001", "https://dx.doi.org/10.1371/journal.pone.0071226.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Early_Prediction_of_Movie_Box_Office_Success_Based_on_Wikipedia_Activity_Big_Data_/779335", "title"=>"Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-08-21 02:15:59"}

PMC Usage Stats | Further Information

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  • {"unique-ip"=>"56", "full-text"=>"44", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"23", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"8"}
  • {"unique-ip"=>"57", "full-text"=>"58", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
  • {"unique-ip"=>"65", "full-text"=>"64", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
  • {"unique-ip"=>"49", "full-text"=>"61", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Neuroscience", "average_usage"=>[261, 444, 554, 655, 748, 834, 923, 1004, 1089, 1170, 1244, 1315, 1380]}, {"subject_area"=>"/Computer and information sciences", "average_usage"=>[297, 488, 616, 724, 828, 939, 1038, 1127, 1223, 1311, 1393, 1479, 1556]}, {"subject_area"=>"/Computer and information sciences/Neural networks", "average_usage"=>[284, 481, 590, 705, 782, 880, 969, 1051, 1141, 1227, 1337, 1399, 1463]}, {"subject_area"=>"/Social sciences/Psychology", "average_usage"=>[294, 460, 580, 683, 777, 868, 957, 1044, 1124, 1202, 1276, 1356, 1422]}, {"subject_area"=>"/Social sciences/Sociology", "average_usage"=>[310, 523, 656, 780, 900, 1024, 1120, 1219, 1308, 1395, 1468, 1570, 1642]}]}
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