Big Data and Fuzzy Based Techniques in Analysis of Social Networks

Abstract

Social networks—a phenomenon of an early 21st century—is perceived as a source of data generated by users about users themselves and things and dealings related to them. Because of that social networks are treated as an object of many academic, corporate and industrial research activities leading to a better understanding of individuals’ behavior, (dis)likes and needs, as well as events and issues important for them. A very active involvement of individuals in social network means that every day millions of new pieces of information is generated. Analysis of this vast amount of available data requires methods and approaches taken from the domain of big data. The theory of fuzzy sets and systems, introduced in 1965, provides the researchers with techniques that are able to cope with imprecise information expressed linguistically. This theory constitutes a basis for designing and developing methodologies of processing data that are able to identify and understand views and judgments expressed in a unique, human way – the core of information generated by the users of social networks. The paper tries to recognize a few important example of extracting value from social network data that can benefit from application of fuzzy set and systems methodology.