Purpose

This 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/approach

This 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.

Findings

The 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/value

The 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 implications

The 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

中文翻译：

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时尚行业的趋势跟踪工具：社交媒体的影响

目的

本文旨在展示利用社交媒体捕捉时尚趋势（包括服装特定功能的流行程度）的价值，以提高快时尚时代供应链响应的速度和准确性。

设计/方法论/途径

本研究探讨了文本挖掘在提高时尚行业趋势识别方面所发挥的作用。研究人员使用n元语法分析设计了一种社交媒体趋势检测工具，此处称为 Twitter 趋势工具 (3Ts)。该工具应用于 Twitter 数据集，以识别趋势，然后根据 Google 趋势检查其有效性。

发现

结果表明，Twitter 数据具有趋势代表性，可用于近乎实时地识别最需要的服装功能。

原创性/价值

本研究中引入的 3T 为时尚分析领域做出了贡献，它提供了一种利用社交媒体大数据识别消费者对时尚元素的偏好并分析消费者偏好以改进需求规划的新颖方法。

实际影响

3T 改进了预测模型，并有助于为服装零售行业（尤其是快时尚行业）的营销活动提供信息。