An aggregation model of online social networks to support group decision-making

Abstract

International audienceOnline social networks, more commonly called social networks, with websites such as Facebook, Twitter or LinkedIn, have become a very important part of our everyday life. An enormous amount of data is increasingly generated by millions of connected users. These data cover lots of personal and social information including users’ profile information, their current topics of interest, mutual relationships and so on. In this paper, we present a new approach for aggregating such available data with the objective of knowledge sharing and group decision support. The proposed system is able to access, gather, filter and integrate relevant information from social networks, more precisely those published by the members of a given group, into a collaborative knowledge system. Gathered information is centralized and thus accessible to other members at a single place. It can also be combined with other types of information like internal collaborative traces (i.e. member interactions, member activities) and be efficiently visualized for supporting group-related decision-making processes

Group consensus measurement in MADM with multiple preference formats

“…Integration is an important task in decision support process (Chiclana et al, 1998;Rao, 2008;Liu et al, 2010;Perçina and Kahramanb, 2010;Vu et al, 2014), as well as it does in group decision making process with preference information on alternatives (Cabrerizo et al, 2013;Chiclana et al, 2013). In group decision making process, multiple experts are always involved and express their preference information on alternatives in different formats due to different culture and education backgrounds, such as, fuzzy preference relations , pairwise comparison(Saaty, 2008, linguistics with different granularities (Mata, et al, 2009).…”