An overview of aggregation methods for social networks analysis

Creating systems that can interpret and manage the ambiguity and subjectivity of the representation and retrieval of information is one of the issues facing information systems researchers. With the emergence of social networks, improvement methods have been developed in both traditional and social information research while taking into account the specificity of the information. The most main features of language that directly impact the results of information systems are ambiguity and uncertainty. We show through this article several approaches have been applied in order to take into account the ambiguity of language, especially in social networks. We thus notice a tendency to apply a variety of aggregation tools in order to overcome the weaknesses of social information retrieval systems. In what follows, we will give an overview on other levels of aggregation allowing to solve certain problems of social information analysis such as credibility evaluation, profile categorization, opinion mining, influencer detection, etc. Then, we held a discussion on the ability of uncertainty theory to consider the different degrees of feature importance as well as the heterogeneity of information resources.