Guest editorial: Recent advances on big social data

Abstract

Big data has been an important issue in information science communities. Recently, limits on the size of datasets that are feasible to process in a reasonable amount of time were on the order of exabytes of data. Scientists in many different domains (e.g. meteorology, genomics, connectomics, complex physics simulations, and biological and environmental research [1, 2]) regularly encounter limitations due to large datasets. In particular, these limitations strongly affect social network applications (e.g. Internet search, finance and business informatics). Social Understanding and Mining has been a complex and hot topic for a large number of research areas in recent years. A society is, in essence, a collection of rational and adaptive individuals taking decisions in a highly interconnected environment, complex, dynamic and non-linear environment. An emergent collective behaviour emerges from such scenario without the necessity to provide specific goals to the users that belong to the group, community or any other kind of social-based structure that the interactions among the users could generate. The rising and huge popularity of Social Network applications (i.e. Facebook or Twitter) have provided an ideal environment to test and simulate new models, algorithms and methods to process knowledge that can be later used to understand (and mine) the behaviours in users or groups [3, 4]. This knowledge can be later used by different agents such as enterprises and private companies, public institutions, or researchers and scholars interested in formal and empirical analysis of social trends. However, these models and techniques could go beyond our current Information Science and Engineering approaches if notable contributions from Social Sciences (i.e. sociology, psychology, e-government, marketing, advertising etc.) are studied and exploited enough [5]. The current gap between theories and models from Social Sciences and advanced methods from Collective Intelligence is still a problem to be solved from both communities. Therefore, this special issue has tried to be a meeting point where researchers in Information Sciences could have the opportunity to present current research results, and to look for new ways of sharing knowledge (techniques, methods, models and theories).