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2020 | OriginalPaper | Buchkapitel

Identifying Trendsetters in Online Social Networks – A Machine Learning Approach

verfasst von : Martina Fricke, Freimut Bodendorf

Erschienen in: Advances in the Human Side of Service Engineering

Verlag: Springer International Publishing

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Abstract

Especially in the fashion business consumers nowadays ask continuously for new styles and products, which forces companies to generate new ideas and innovative products faster than ever. Besides, consumers interact with each other on social media platforms and exchange their problems, needs, preferences, and ideas. With 95 Mio. daily postings on Instagram, it becomes obvious that such a platform is a huge data source containing important and valuable information regarding product requirements, customer tastes and needs, and upcoming trends. This paper presents a model to identify trendsetters based on their social media profiles and interactions on Instagram by using multiple machine learning classifiers. The model is trained with data of 665 user accounts, considering 59 features. Maximum Entropy Model performs the best with a F1-score of 66.67%.

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Synthetic Minority Oversampling Technique.
 
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Metadaten
Titel
Identifying Trendsetters in Online Social Networks – A Machine Learning Approach
verfasst von
Martina Fricke
Freimut Bodendorf
Copyright-Jahr
2020
Buch
Advances in the Human Side of Service Engineering

Print ISBN: 978-3-030-51056-5

Electronic ISBN: 978-3-030-51057-2

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-51057-2_1

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