Title: Fuzzy soft set decision-making model for social networking sites

Authors: Dharmendra Singh Rajput; Neelu Khare

Addresses: Department of Software and Systems Engineering, School of Information Technology and Engineering, VIT University, Vellore, 632014, India ' Department of Software and Systems Engineering, School of Information Technology and Engineering, VIT University, Vellore, 632014, India

Abstract: The 'social media' has become synonymous of today's generation. Approximately two third of Indians online spend time on different social networking sites like Facebook, Twitter, YouTube, Whatsapp, Qzone, Google+, Snapchat, Pinterest, etc. Interaction, live chat, status updates, image- as well as video-sharing are few of the major aspects that play a role in the popularity of social media. This popularity provides an opportunity to study and analyse the characteristics of online social network graphs at large scale. Understanding these graphs is important to improve current systems and to design new means to determine them by important parameters such as: security, reliability, value added features, connectivity to other online social networks, etc. Hence, social network analysis (SNA) is becoming a vital tool for researchers, but all the necessary information is often available in a distributed environment. This paper presents fuzzy soft set decision-making model, which gives a new hypothesis for determining the popular social networking sites by involving significant parameters. The model has applied fuzzy soft set theory on 14 significant parameters to predict the popularity of social networking sites. The experimental result shows that the FSS decision-making model provides a new algorithm which is to determine the most popular networking site.

Keywords: fuzzy soft sets; decision values; impact indicators; divide factors; discrimination factors; rough sets; social network analysis; SNA; fuzzy logic; decision making; social media; social networks.

DOI: 10.1504/IJSNM.2016.082644

International Journal of Social Network Mining, 2016 Vol.2 No.3, pp.256 - 266

Received: 01 Oct 2015
Accepted: 26 May 2016
Published online: 04 Mar 2017 *

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