Big Data Meets Social Networks: A Survey of Analytical Strategies and Research Challenges

As the big data era transforms the information analysis landscape, social network (SN) analytics has emerged as a critical discipline to understand complex relationships and interactions within enormous social systems. This study aims to provide a comprehensive overview of SN analytics as big data, providing an in-depth exploration of concepts, methods, and applications within this rapidly evolving field. The survey covers a wide range of topics, including methods for pre-processing and cleaning social network data, as well as more advanced analytical approaches such as opinion mining, sentiment analysis, text mining, and natural language processing. It also discusses various big data architectures for SN analytics, like Lambda architecture, Kappa architecture, and GraphX architecture. In addition, it covers issues of scalability, data quality, privacy, and real-time analysis. SN analytics can be useful for researchers from across domains like business and marketing, healthcare and biomedicine, social sciences and psychology, politics and governance, cybersecurity and fraud detection, recommendation systems, crisis management, and disaster response to extract crucial information from vast datasets and formulate domain-specific strategies.