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Conference Paper: Visual Communication and Fashion Popularity Contagion in Social Networks
Title | Visual Communication and Fashion Popularity Contagion in Social Networks |
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Authors | |
Keywords | deep learning fast fashion social contagion Social media |
Issue Date | 2022 |
Citation | International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation", 2022 How to Cite? |
Abstract | Fast 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 Identifier | http://hdl.handle.net/10722/352436 |
DC Field | Value | Language |
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dc.contributor.author | Xiang, Li | - |
dc.contributor.author | Yunhui, Wang | - |
dc.contributor.author | Junming, Liu | - |
dc.contributor.author | Hui, Xiong | - |
dc.date.accessioned | 2024-12-16T03:58:56Z | - |
dc.date.available | 2024-12-16T03:58:56Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation", 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10722/352436 | - |
dc.description.abstract | Fast 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.language | eng | - |
dc.relation.ispartof | International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation" | - |
dc.subject | deep learning | - |
dc.subject | fast fashion | - |
dc.subject | social contagion | - |
dc.subject | Social media | - |
dc.title | Visual Communication and Fashion Popularity Contagion in Social Networks | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-85192565369 | - |