Identifying trendsetters in online social networks – a machine learning approach - Fricke, Martina; Bodendorf, Freimut (Advances in Intelligent Systems and Computing, 2020)
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%.
CRIS-Team Scopus Importer:2020-08-07
Keywords: Machine learning; Online social networks; Trendsetter