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

2020 ◽  
Author(s):  
Lisa Parillo-Chapman ◽  
Marguerite Moore ◽  
Yanan Yu
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Fashion Industry

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

2018 ◽  
Vol 37 (2) ◽  
pp. 87-102 ◽  
Author(s):  
Li Zhao ◽  
Chao Min
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Dynamic Network ◽  
Fashion Industry ◽  
Cognitive Computing ◽  
Data Set

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.

Social Network Analysis

2018 ◽  
pp. 1-18 ◽  
Author(s):  
Somya Jain ◽  
Adwitiya Sinha
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Data Analysis ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Analysis Techniques ◽  
Text Images ◽  
Application Specific

Over the last decade, technology has thrived to provide better, quicker, and more effective platforms to help individuals connect and disseminate information to other individuals. The increasing popularity of these networks and its huge content in the form of text, images, and videos provides new opportunities for data analytics in the context of social networks. This motivates data mining experts and researchers to deploy various mining apparatus and application-specific tools for analysing the massive, intricate, and dynamic social media knowledge. The research detailed in this chapter would entail major social network concepts with data analysis techniques. Moreover, it gives insight to representation and modelling of social networks with research datasets and tools.

scholarly journals Emergent use of Social Media on Elections: the use of Data Mining and Social Network Analysis for Political Purposes

2021 ◽  
Vol 10 (6) ◽  
pp. 118-122
Author(s):  
Acharoui Zakia ◽  
Ettaki Badia ◽  
Zerouaoui Jamal
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Support Vector ◽  
Use Of Data ◽  
Election Results ◽  
Use Of Social Media ◽  
The Impact

People spend more time on social media either for personal or social interest which generates an expanding amount of Data. This paper is written for researchers seeking to have an overview of the different technical methods used for political purposes principally Data Mining and Social Network Analysis. Hence, the first part introduces the impact of Social Media on politics for different aims such as communicating with voters, promoting participation, and predicting election results, then the two main methods to achieve political purposes were presented. Data mining approaches is likely to be used on political context to classify citizen’s opinion or predicting results thus by using methods such as term occurrence, mentions, Support Vector Machine, Machine Learning, and Artificial Neural Networks. The Social Network Analysis approaches are used to retrieve data about influencers, their role during a period, and the nature of the information shared.

Social network analysis of an emerging innovation: direct-to-garment printing technology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yanan Yu ◽  
Marguerite Moore ◽  
Lisa P. Chapman
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Computational Technique ◽  
Fashion Industry ◽  
Printing Technology ◽  
Content Type ◽  
Context Specific ◽  
Insight Into

PurposeThe study primarily aims to examine an emerging fashion technology, direct-to-garment (DTG) printing, using data mining-driven social network analysis (SNA). Simultaneously, the study also demonstrates application of a group novel computational technique to capture, analyze and visually depict data for strategic insight into the fashion industry.Design/methodology/approachA total of 5,060 tweets related to DTG were captured using Crimson Hexagon. Python and Gephi were applied to convert, calculate and visualize the yearly networks for 2016–2019. Based on graph theory, degree centrality and betweenness centrality indices guide interpretation of the outcome networks.FindingsThe findings reveal insights into DTG printing technology networks through identification of interrelated indicators (i.e. nodes, edges and communities) over time. Deeper interpretation of the dominant indicators and the unique changes within each of the DTG communities were investigated and discussed.Practical implicationsThree SNA models suggest directions including the dominant apparel categories for DTG application, competing alternatives for apparel decorating approaches to DTG and growing market niches for DTG. Interpretation of the yearly networks suggests evolution of this domain over the investigation period.Originality/valueThe social media based, data mining-driven SNA method provides a novel path and a powerful technique for scholars and practitioners to investigate information among complex, abstract or novel topics such as DTG. Context specific findings provide initial insight into the evolving competitive structures driving DTG in the fashion market.

ANALISIS PERINGKAT TOP BRAND OJEK ONLINE MENGGUNAKAN JEJARING SOSIAL PERCAKAPAN TWITTER

2019 ◽  
Vol 24 (2) ◽  
pp. 88-104
Author(s):  
Ilham Aminudin ◽  
Dyah Anggraini
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Path Length ◽  
Average Path Length ◽  
Average Degree ◽  
Clustering Coefficient ◽  
R Programming

Banyak bisnis mulai muncul dengan melibatkan pengembangan teknologi internet. Salah satunya adalah bisnis di aplikasi berbasis penyedia layanan di bidang moda transportasi berbasis online yang ternyata dapat memberikan solusi dan menjawab berbagai kekhawatiran publik tentang layanan transportasi umum. Kemacetan lalu lintas di kota-kota besar dan ketegangan publik dengan keamanan transportasi umum diselesaikan dengan adanya aplikasi transportasi online seperti Grab dan Gojek yang memberikan kemudahan dan kenyamanan bagi penggunanya Penelitian ini dilakukan untuk menganalisa keaktifan percakapan brand jasa transportasi online di jejaring sosial Twitter berdasarkan properti jaringan. Penelitian dilakukan dengan dengan mengambil data dari percakapan pengguna di social media Twitter dengan cara crawling menggunakan Bahasa pemrograman R programming dan software R Studio dan pembuatan model jaringan dengan software Gephy. Setelah itu data dianalisis menggunakan metode social network analysis yang terdiri berdasarkan properti jaringan yaitu size, density, modularity, diameter, average degree, average path length, dan clustering coefficient dan nantinya hasil analisis akan dibandingkan dari setiap properti jaringan kedua brand jasa transportasi Online dan ditentukan strategi dalam meningkatkan dan mempertahankan keaktifan serta tingkat kehadiran brand jasa transportasi online, Grab dan Gojek.

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