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

Date
2018-01-01
Authors
Zhao, Li
Min, Chao
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Department
Abstract

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.

Comments
Description
Keywords
Citation
DOI
Source