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Conference Paper: Visual Communication and Fashion Popularity Contagion in Social Networks

TitleVisual Communication and Fashion Popularity Contagion in Social Networks
Authors
Keywordsdeep learning
fast fashion
social contagion
Social media
Issue Date2022
Citation
International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation", 2022 How to Cite?
AbstractFast fashion has emerged as a prevalent retail strategy shaping fashion popularity. However, due to the lack of historical records and the dynamics of fashion trends, existing demand prediction methods do not apply to new-season fast fashion sales forecasting. We draw on the Social Contagion Theory to conceptualize a sales prediction framework for fast fashion new releases. We posit that fashion popularity contagion comes from Source Contagion and Media Contagion, which refer to the inherent infectiousness of fashion posts and the popularity diffusion in social networks, respectively. We consider fashion posts as the contagion source that visually attracts social media users with images of fashion products. Graph Convolutional Network is developed to model the dynamic fashion contagion process in the topology structure of social networks. This theory-based deep learning method can incorporate the latest social media activities to offset the deficiency of historical fashion data in new seasons.
Persistent Identifierhttp://hdl.handle.net/10722/352436

 

DC FieldValueLanguage
dc.contributor.authorXiang, Li-
dc.contributor.authorYunhui, Wang-
dc.contributor.authorJunming, Liu-
dc.contributor.authorHui, Xiong-
dc.date.accessioned2024-12-16T03:58:56Z-
dc.date.available2024-12-16T03:58:56Z-
dc.date.issued2022-
dc.identifier.citationInternational Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation", 2022-
dc.identifier.urihttp://hdl.handle.net/10722/352436-
dc.description.abstractFast fashion has emerged as a prevalent retail strategy shaping fashion popularity. However, due to the lack of historical records and the dynamics of fashion trends, existing demand prediction methods do not apply to new-season fast fashion sales forecasting. We draw on the Social Contagion Theory to conceptualize a sales prediction framework for fast fashion new releases. We posit that fashion popularity contagion comes from Source Contagion and Media Contagion, which refer to the inherent infectiousness of fashion posts and the popularity diffusion in social networks, respectively. We consider fashion posts as the contagion source that visually attracts social media users with images of fashion products. Graph Convolutional Network is developed to model the dynamic fashion contagion process in the topology structure of social networks. This theory-based deep learning method can incorporate the latest social media activities to offset the deficiency of historical fashion data in new seasons.-
dc.languageeng-
dc.relation.ispartofInternational Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation"-
dc.subjectdeep learning-
dc.subjectfast fashion-
dc.subjectsocial contagion-
dc.subjectSocial media-
dc.titleVisual Communication and Fashion Popularity Contagion in Social Networks-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-85192565369-

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