Big Data Analyses for Collective Opinion Elicitation in Social Networks
Yingxu Wang, Victor J. Wiebe · 2014
Big data are extremely large-scaled data in terms of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. Censuses and elections are a typical paradigm of big data engineering in modern digital democracy and social networks. This paper analyzes the mechanisms of voting systems and collective opinions using big data analysis technologies. A set of numerical and fuzzy models for collective opinion analyses is presented for applications in social networks, online voting, and general elections. A fundamental insight on the collective opinion equilibrium is revealed among electoral distributions and in voting systems. Fuzzy analysis methods for collective opinions are rigorously developed and applied in poll data mining, collective opinion determination, and quantitative electoral data processing.