Camera eats first

Detalhes bibliográficos
Autor(a) principal: Gambetti, Alessandro
Data de Publicação: 2022
Outros Autores: Han, Qiwei
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/141860
Resumo: The authors acknowledge financial support from Fundação para a Ciência e Tecnologia (UID/ECO/00124/2019) by LISBOA-01-0145-FEDER007722 and Social Sciences Data Lab PINFRA/22209/2016
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spelling Camera eats firstExploring food aesthetics portrayed on social media using deep learningFood aestheticsGastronomic experienceSocial mediaComputer versionDeep learningThe authors acknowledge financial support from Fundação para a Ciência e Tecnologia (UID/ECO/00124/2019) by LISBOA-01-0145-FEDER007722 and Social Sciences Data Lab PINFRA/22209/2016Purpose The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images. Design/methodology/approach A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image. Findings This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, the authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former. Practical implications Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images. Originality/value The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.NOVA School of Business and Economics (NOVA SBE)RUNGambetti, AlessandroHan, Qiwei2022-07-13T22:27:36Z2022-08-242022-08-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/141860eng0959-6119PURE: 45350533https://doi.org/10.1108/IJCHM-09-2021-1206info: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:RCAAP2024-03-11T05:19:11Zoai:run.unl.pt:10362/141860Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:50:04.751198Repositório 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 Camera eats first
Exploring food aesthetics portrayed on social media using deep learning
title Camera eats first
spellingShingle Camera eats first
Gambetti, Alessandro
Food aesthetics
Gastronomic experience
Social media
Computer version
Deep learning
title_short Camera eats first
title_full Camera eats first
title_fullStr Camera eats first
title_full_unstemmed Camera eats first
title_sort Camera eats first
author Gambetti, Alessandro
author_facet Gambetti, Alessandro
Han, Qiwei
author_role author
author2 Han, Qiwei
author2_role author
dc.contributor.none.fl_str_mv NOVA School of Business and Economics (NOVA SBE)
RUN
dc.contributor.author.fl_str_mv Gambetti, Alessandro
Han, Qiwei
dc.subject.por.fl_str_mv Food aesthetics
Gastronomic experience
Social media
Computer version
Deep learning
topic Food aesthetics
Gastronomic experience
Social media
Computer version
Deep learning
description The authors acknowledge financial support from Fundação para a Ciência e Tecnologia (UID/ECO/00124/2019) by LISBOA-01-0145-FEDER007722 and Social Sciences Data Lab PINFRA/22209/2016
publishDate 2022
dc.date.none.fl_str_mv 2022-07-13T22:27:36Z
2022-08-24
2022-08-24T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/141860
url http://hdl.handle.net/10362/141860
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0959-6119
PURE: 45350533
https://doi.org/10.1108/IJCHM-09-2021-1206
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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instacron:RCAAP
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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
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