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Identifying trendsetters in online social networks – a machine learning approach

Fricke M, Bodendorf F (2020)

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Publication Type: Conference contribution

Publication year: 2020

Publisher: Springer

Book Volume: 1208 AISC

Pages Range: 3-9

Conference Proceedings Title: Advances in Intelligent Systems and Computing

Event location: San Diego, CA

ISBN: 9783030510565

DOI: 10.1007/978-3-030-51057-2_1

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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How to cite

APA:

Fricke, M., & Bodendorf, F. (2020). Identifying trendsetters in online social networks – a machine learning approach. In Jim Spohrer, Christine Leitner (Eds.), Advances in Intelligent Systems and Computing (pp. 3-9). San Diego, CA, US: Springer.

MLA:

Fricke, Martina, and Freimut Bodendorf. "Identifying trendsetters in online social networks – a machine learning approach." Proceedings of the AHFE Virtual Conference on the Human Side of Service Engineering, 2020, San Diego, CA Ed. Jim Spohrer, Christine Leitner, Springer, 2020. 3-9.

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