Abstract
PurposeThis paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.Design/methodology/approachThis study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.FindingsThe results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.Originality/valueThe 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.Practical implicationsThe 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.
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