Track

TAI

Presentation Type

Oral

Description

With the advent of modern cognitive computing technologies, fashion informatics research contributes to the academic and professional discussion about how a large-scale dataset is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area to investigate relations and information flow among fashion units. By adopting this pragmatic approach, this study provides dynamic network visualizations of the case of Paris Fashion Week. Three-time periods were researched to monitor the formulation and mobilization of social media users' discussions of the event. Initial textual data on social media were crawled, converted, calculated and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS
 
Jan 1st, 12:00 AM

The Rise of Fashion Informatics: Data-Mining-Based Social Network Analysis in Fashion

With the advent of modern cognitive computing technologies, fashion informatics research contributes to the academic and professional discussion about how a large-scale dataset is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area to investigate relations and information flow among fashion units. By adopting this pragmatic approach, this study provides dynamic network visualizations of the case of Paris Fashion Week. Three-time periods were researched to monitor the formulation and mobilization of social media users' discussions of the event. Initial textual data on social media were crawled, converted, calculated and visualized by Python and Gephi. The most influential nodes (hashtags) that function as junctions and the distinct hashtag communities were identified and represented visually as graphs. The relations between the contextual clusters and the role of junctions in linking these clusters were investigated and interpreted.

 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.