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Identifying Trendsetters in Online Social Networks – A Machine Learning Approach

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Advances in the Human Side of Service Engineering (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1208))

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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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Notes

  1. 1.

    https://github.com/ping/instagram_private_api, https://instaloader.github.io/.

  2. 2.

    Synthetic Minority Oversampling Technique.

  3. 3.

    Linear Support Vector Classification.

  4. 4.

    scikit-learn: http://scikit-learn.org/.

  5. 5.

    imbalanced-learn: http://contrib.scikit-learn.org/imbalanced-learn/stable/index.html#.

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Authors and Affiliations

  1. Friedrich-Alexander University Erlangen-Nuremberg, Lange Gasse 20, 90403, Nuremberg, Germany

    Martina Fricke & Freimut Bodendorf

Authors
  1. Martina Fricke
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  2. Freimut Bodendorf
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Correspondence to Martina Fricke .

Editor information

Editors and Affiliations

  1. IBM Research - Almaden, San Jose, CA, USA

    Jim Spohrer

  2. Centre for Economics and Public Administration, London, UK

    Christine Leitner

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Fricke, M., Bodendorf, F. (2020). Identifying Trendsetters in Online Social Networks – A Machine Learning Approach. In: Spohrer, J., Leitner, C. (eds) Advances in the Human Side of Service Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1208. Springer, Cham. https://doi.org/10.1007/978-3-030-51057-2_1

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51056-5

  • Online ISBN: 978-3-030-51057-2

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