The trend to fast fashion forces companies to create new products and collections faster than ever. Consumers nowadays ask for continuously new styles instead of traditional bi-annual seasonal fashion collections. At the same time, due to the increasing usage of social networks and the resulting higher reach and faster way of communication, consumers have more power in the fashion trend process. This rising complexity and speed in the fashion trend diffusion process forces the textile industry to adapt their methods of trend forecasting, prediction and scouting to stay competitive and to meet consumer needs and preferences nowadays.
This research project aims to identify fashion trendsetters on social networks by analyzing public Instagram data in order to use them as data source for trend prediction. The main goal is to increase the prediction quality by considering the “right” user groups and recognizing “weak signals” of upcoming fashion trends in their postings. The contribution of the research project consists of transferring theories of trend diffusion and social influence to the context of social network platforms as well as the identification of fashion trendsetters by behavioral social media patterns.