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2024 | OriginalPaper | Buchkapitel

Using Social Media Analytics for Extracting Fashion Trends of Preowned Fashion Clothes

verfasst von : Noushin Mohammadian, Nusrat Jahan Raka, Meriel Wanyonyi, Yilmaz Uygun, Omid Fatahi Valilai

Erschienen in: Advances in Intelligent Manufacturing and Service System Informatics

Verlag: Springer Nature Singapore

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Abstract

In recent years, the cloth industry has faced fast fashion trends. This has resulted in Fast fashion as a supply chain model for clothing and accessories that is supposed to respond quickly to the latest fashion trends by frequently updating the products already available in the inventory. However, Fast Fashion has created serious challenges for the sustainability of the clothing industry. This paper investigates the use of social media analytics to understand fashion trends in the preowned fashion industry. The study aims to establish a link between environmental pollution and fast fashion by investigating the preowned fashion industry from both consumer and business perspectives. To achieve this, the study proposes a social analytics (SA) approach to analyze social media posts and predict preowned fashion trends. By using SA techniques, the study hopes to provide valuable insights into consumer behavior and preferences in the preowned fashion industry, which can be used to promote sustainable fashion practices and reduce environmental pollution. Overall, the study demonstrates the potential of social media analytics in understanding and predicting fashion trends, with the goal of promoting sustainable fashion practices.

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Vorheriges Kapitel Internet of Medical Things (IoMT): An Overview and Applications

Nächstes Kapitel An Ordered Flow Shop Scheduling Problem

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Titel: Using Social Media Analytics for Extracting Fashion Trends of Preowned Fashion Clothes
verfasst von: Noushin Mohammadian
Nusrat Jahan Raka
Meriel Wanyonyi
Yilmaz Uygun
Omid Fatahi Valilai
Verlag: Springer Nature Singapore
Buch: Advances in Intelligent Manufacturing and Service System Informatics
Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-6062-0_15

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