The Potential of Automated Text Analytics in Social Knowledge Building

There are still many sociologists who are skeptical of the findings of big data-based analysis of social-data, questioning the potential of this knowledge production and its contribution to the scientific discourse of sociology.The chapter shows that this tension can be addressed through the redefinition of the research methodological basis of sociology, by the organic incorporation of data science know-how into its methods; the combined application of qualitative and quantitative analysis; and, the use of knowledge-driven science instead of the data-driven approach.The theoretical, methodological, and topical pathways between traditional and computational sociology emerge gradually along the chapter, which also includes plenty of illustrative examples of research situated at the interplay between sociology and data science. As our overview shows, there are new possibilities for sociological research, which are, in some sense, just by-products of information science. We introduce recently developed methods, which can be applied to specific sociological problems outside the scope of business applications. We present sociological topics not yet studied in this area and show new insights the approach can offer to classical sociological questions. As our aim is to encourage sociologists to enter this field, we discuss the new methods on the base of the classic quantitative approach, using its concepts and terminology and addressing the question of how traditionally trained sociologists can acquire new skills.

A Two-Tier Partition Algorithm for the Optimization of the Large-Scale Simulation of Information Diffusion in Social Networks

As online social networks play a more and more important role in public opinion, the large-scale simulation of social networks has been focused on by many scientists from sociology, communication, informatics, and so on. It is a good way to study real information diffusion in a symmetrical simulation world by agent-based modeling and simulation (ABMS), which is considered an effective solution by scholars from computational sociology. However, on the one hand, classical ABMS tools such as NetLogo cannot support the simulation of more than thousands of agents. On the other hand, big data platforms such as Hadoop and Spark used to study big datasets do not provide optimization for the simulation of large-scale social networks. A two-tier partition algorithm for the optimization of large-scale simulation of social networks is proposed in this paper. First, the simulation kernel of ABMS for information diffusion is implemented based on the Spark platform. Both the data structure and the scheduling mechanism are implemented by Resilient Distributed Data (RDD) to simulate the millions of agents. Second, a two-tier partition algorithm is implemented by community detection and graph cut. Community detection is used to find the partition of high interactions in the social network. A graph cut is used to achieve the goal of load balance. Finally, with the support of the dataset recorded from Twitter, a series of experiments are used to testify the performance of the two-tier partition algorithm in both the communication cost and load balance.

Computation and the Sociological Imagination

Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.

COMPUTATIONAL SOCIOLOGY: APPLICATION AND DEVELOPMENT

Along with the popularity of research on Big Data and the increasingly massive use of computer as well as internet-based research, the development of studies with a computational sociology approach has also received more attention. Computational sociology approach such as agent-based modeling or social network analysis has become a new landmark of the interest of social scientist around the world to continue develops suitable interdisciplinary research approaches. This article makes mapping and visualizing the trends and developments of computational sociology studies through a systematic review and bibliometric analysis of scientific publications in the Scopus database. Concepts and clusters of studies on contemporary computational sociology are discussed, including international journals that have a focus and interest in this approach and research area.

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

Massive Cyber-Attacks Patterns Implemented with BDI Agents

This paper treats the new phenomenon of cyber-war as a new form of human violence between states. The concept of cyber-attack is a particular form of cybernetic war and it is one expensive in terms of cost. We tried to achieve a uniform approach of technological and sociological type using BDI agents and thus making the step to addressing computational sociology of cyberspace. We present two patterns of massive type attacks applicable in cyber space using BDI agents. The approach using BDI agents turned to be flexible and capable to model complex forms of cyber bullying that can occur between two countries in future.

Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.