Reading Fashion Through Data

Fashion is not determined solely by the design of clothing.

It is shaped through the interplay of various elements, including brand narratives, people’s opinions and evaluations shared on social media, visual features such as color and form, and the broader cultural atmosphere of a given time.

These elements exist as different forms of “data.”

Text, numbers, visual features, tags, temporal dynamics, and social connections may differ in format, but all of them reflect aspects of fashion.

However, behind these processes, several key questions remain insufficiently understood:

• Do consumer-driven trends truly exist?

• How do they relate to brand-driven trends?

• How are fashion styles generated, how do they spread, and how do they evolve over time?

In other words, the mechanisms through which trends emerge and develop are still not clearly visible.

Therefore, this research aims to integrate and analyze diverse data sources to uncover the mechanisms behind the generation, diffusion, and transformation of fashion trends and styles,

and to organize these insights into a new framework for understanding and analyzing fashion as a dynamic, data-driven phenomenon shaped by interactions among people, information, and culture.