Computing Reviews

Social big data analytics: practices, techniques, and applications
Abu-Salih B., Wongthongtham P., Zhu D., Yan Chan K., Rudra A., Springer International Publishing,Cham, Switzerland,2021. 228 pp.Type:Book
Date Reviewed: 11/08/22

Thanks to the advances in wireless sensor communication, large-scale storage, and computing technologies, those who can afford to be in one or more of the available platforms--the Internet, WhatsApp, or Twitter, to name a few--are connected directly (or at most through two intermediaries) instead of the old paradigm of six degrees of separation. This increase in connectivity has resulted in an explosion of information sharing. This book is a first attempt to present a consolidated view of the emerging domain, that is, an analysis of the huge volumes of data generated within social interaction, hence the title Social big data analytics.

The book is divided into seven chapters, each of moderate size. After providing a broad overview of what social big data is in chapter 1, including several of its applications, chapter 2 contains a comprehensive introduction to various components of the technology that is responsible for this revolution. Today, a time when it is very difficult to separate what is fake from the truth, chapter 3 provide a good discussion of credibility analysis using the notion of trustworthiness. Methods for semantic analysis are covered in chapter 4. The role of machine learning in the development of predictive tools germane to social big data is reviewed in chapter 5. The role of the influence of design to meet customer needs and demands is the theme of chapter 6. The last chapter deals with the ever-important topic of sentiment analysis in social media and provides a good bridge to deep-learning-based approaches. There is ample reference to the literature at the end of each chapter. There is no explicit set of exercises and no index, but navigation can be done through the contents page.

This book is accessible to a wide audience: senior-level students at business schools and arts, sciences, and engineering colleges. After a short introduction to basic machine learning tools--such as classification, regression, and neural networks combined with the knowledge of using canned programs--this book can be used as a basis for an elective upper-level course supplemented by class projects involving real-world data. The book can also be gainfully read by professionals working in this area.

In summary, this is a timely addition to the growing and important area of social data analysis.

Reviewer:  S. Lakshmivarahan Review #: CR147511

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