The big four: discrete choice modelling to predict the four major Oscar categories

Detalhes bibliográficos
Autor(a) principal: Afonso, Pedro Neves Barriga
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/32468
Resumo: The present study formulates regression models that predict the four major Oscar categories (Picture, Director, Actor and Actress). A database was created, collecting publicly available information from 2005 to 2016. The approach taken was to apply discrete choice modelling. A remarkable predictive accuracy was achieved, as every single Oscar winner was correctly predicted. The study found evidence of the crucial role of directors, the predictive power of box office, gender discrepancies in the film industry and the Academy’s biases in the selection of winners related to the film genre, nominees’ body of work and the portrayal of actual events.
id RCAP_87d91f7cdc4064f8c7058ec2a3d7ab9d
oai_identifier_str oai:run.unl.pt:10362/32468
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling The big four: discrete choice modelling to predict the four major Oscar categoriesPredictionOscarsCinemaBinary choice modelsDomínio/Área Científica::Ciências Sociais::Economia e GestãoThe present study formulates regression models that predict the four major Oscar categories (Picture, Director, Actor and Actress). A database was created, collecting publicly available information from 2005 to 2016. The approach taken was to apply discrete choice modelling. A remarkable predictive accuracy was achieved, as every single Oscar winner was correctly predicted. The study found evidence of the crucial role of directors, the predictive power of box office, gender discrepancies in the film industry and the Academy’s biases in the selection of winners related to the film genre, nominees’ body of work and the portrayal of actual events.Rodrigues, Paulo Manuel MarquesRUNAfonso, Pedro Neves Barriga2021-01-20T01:30:26Z2018-01-202018-01-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/32468TID:201861500enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:18:04Zoai:run.unl.pt:10362/32468Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:29:52.591538Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv The big four: discrete choice modelling to predict the four major Oscar categories
title The big four: discrete choice modelling to predict the four major Oscar categories
spellingShingle The big four: discrete choice modelling to predict the four major Oscar categories
Afonso, Pedro Neves Barriga
Prediction
Oscars
Cinema
Binary choice models
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short The big four: discrete choice modelling to predict the four major Oscar categories
title_full The big four: discrete choice modelling to predict the four major Oscar categories
title_fullStr The big four: discrete choice modelling to predict the four major Oscar categories
title_full_unstemmed The big four: discrete choice modelling to predict the four major Oscar categories
title_sort The big four: discrete choice modelling to predict the four major Oscar categories
author Afonso, Pedro Neves Barriga
author_facet Afonso, Pedro Neves Barriga
author_role author
dc.contributor.none.fl_str_mv Rodrigues, Paulo Manuel Marques
RUN
dc.contributor.author.fl_str_mv Afonso, Pedro Neves Barriga
dc.subject.por.fl_str_mv Prediction
Oscars
Cinema
Binary choice models
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Prediction
Oscars
Cinema
Binary choice models
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description The present study formulates regression models that predict the four major Oscar categories (Picture, Director, Actor and Actress). A database was created, collecting publicly available information from 2005 to 2016. The approach taken was to apply discrete choice modelling. A remarkable predictive accuracy was achieved, as every single Oscar winner was correctly predicted. The study found evidence of the crucial role of directors, the predictive power of box office, gender discrepancies in the film industry and the Academy’s biases in the selection of winners related to the film genre, nominees’ body of work and the portrayal of actual events.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-20
2018-01-20T00:00:00Z
2021-01-20T01:30:26Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/32468
TID:201861500
url http://hdl.handle.net/10362/32468
identifier_str_mv TID:201861500
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799137923878092800

Registros relacionados