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

Abstract

With the advent of modern cognitive computing technologies, fashion informatics researchers contribute to the academic and professional discussion about how a large-scale data set is able to reshape the fashion industry. Data-mining-based social network analysis is a promising area of fashion informatics to investigate relations and information flow among fashion units. By adopting this pragmatic approach, we provide 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.

Semantic Network Analysis to Explore the Concept of Sustainability in the Apparel and Textile Industry

“…Consumers actively participate in various social media platforms to exchange their thoughts, opinions, and information about fashion purchases and consumption [60]. Recently, the number of meaningful network studies using big data of social media has increased due to the advantage of obtaining more customer-centered and in-depth information compared to the results derived from surveys or interviews with consumers [21,61,62]. Big data refers to large-scale data including unstructured data, such as text and video data, as well as large-scale, digitized data generated within digital environments such as the Internet and SNS [61].…”

“…Recently, the number of meaningful network studies using big data of social media has increased due to the advantage of obtaining more customer-centered and in-depth information compared to the results derived from surveys or interviews with consumers [21,61,62]. Big data refers to large-scale data including unstructured data, such as text and video data, as well as large-scale, digitized data generated within digital environments such as the Internet and SNS [61]. The most important key to the fashion industry is the internal needs of consumers, and research based on social big data has been conducted to analyze consumers' voices in-depth.…”

“…As a result, there have been many changes in the data and analysis methods used in research [51]. Many programs have been used to collect, analyze, and visualize data on social media with numerous ways to present the results [61,62]. Furthermore, research using big data analysis methods shows that the scope of research has expanded both quantitatively and qualitatively beyond the limitations of the small number of people in the study and the boundaries of accessibility to survey responses [22].…”

“…Text mining, a method to extract extensive knowledge from unstructured text in SNS, has been conducted using natural language processing, a form of machine learning that takes massive amounts of language text and converts it into a useful form for researchers or practitioners by mining and extracting important keywords [62,64]. Semantic network analysis reveals knowledge and information about semantic networks between words in a network by demonstrating the information in a visual graph with labeled nodes [61]. Principally, any words in semantic networks that may be related to other words can be deemed nodes [62].…”

Socialization on Sustainable Networks: The Case of eBay Green’s Facebook

“…They are becoming active participants in their engaged networks, and are aggressively seeking relationships and sharing knowledge with other entities. Recently, a few fashion informatic approaches have been initiated to identify this type of consumer social dynamic by employing big data and advanced analytic skills [10] (Zhao & Min, 2019). Aligning data informatics efforts with marketing, design, store operations, and merchandising could enable the F & T field to understand and connect to customers in more meaningful ways than ever before.…”