Social Media Based, Data-mining Driven Social Network Analysis (SNA) of Printing Technologies in Fashion Industry

Abstract

The dynamic supply of online information with millions of social media messages derived from human activities is difficult to analyze using conventional methodologies. This study demonstrates application of data-mining driven Social Network Analysis to generate a model of four predominant printing terms (i.e., screen printing, heat transfer, sublimation, and digital printing) that emerged from earlier network analyses. A total of 3,000 random tweets related to four printing terms were captured using Crimson Hexagon. Python and Gephi were applied to convert, calculate and visualize the network. Based on graph theory, degree centrality and betweenness centrality indices guide interpretation of the outcome network. The findings reveal insights into different printing technologies through identification of interrelated indicators and enable us to build a foundational understanding of the opaque fashion printing market. Simultaneously, the study demonstrates a process for examining un-defined, emerging technology that is not understood among brands or consumers.