Researchers said that analyzing real time data from Instagram and Twitter can help predict the fashion trends and even identify the next popular designers. Researchers can identify the network of influence among designers by analyzing relevant phrases and words from fashion reviews. They can also keep track of how the trends moved through the industry. This was according to the Heng Xu, an associate professor of information sciences and technology from Penn State University.

Researchers have analyzed more than 6,000 runway reviews of 800+ designers from, which was the former online site for Vogue. The reviews covered fashion seasons from 2000 to 2014.

Professor Xu said that they got keywords and phrases from the reviews that described the colors, silhouettes, and other details of the collections and added them to their database. Then they ranked the designers and mapped influences within the group.

New fashion trends based on social media

In order to find the accuracy of the model, they compared their map with three industry-recognized lists of influential designers and they discovered that their model matched with the lists.

Xu said that there is no gold standard when it comes to influential designers. They believe that their work is a good place to start comparing data. Fashion designers are still skeptical about data analytics, unlike professionals from other industries. Designers see their work as an art form that they see as something that’s difficult to quantify.

Yilu Zhou, an associate profession from Fordham University, worked with Xu on the research. She said that they found data that are clues that can be traced back to the individual designers. They said that technology could help professionals to predict fashion trends and find up-and-coming designers.

Zhou said that everyone knows about the major designers today but the technology can be used to discover the next big fashion designers. It can also be used to predict trends in the industry.

Social media dataflows by Anne Helmond, on Flickr
Creative Commons Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 Generic License   Photo by  Anne Helmond