Composed of two parts, “Social Behavior Under Normal Conditions” and “Social Behavior Under Stress,” it is really unique and interesting as a data analysis book titled like a classical social science book. In addition to the introductory chapter, there are seven chapters in the first part and four chapters in the second part. Each chapter (except the last) is written by a team from various disciplines. As an example, the authors of the second chapter, “Modeling and Understanding Intrinsic Characteristics of Human Mobility,” are from systems engineering, the Massachusetts Institute of Technology (MIT) Media Lab, and civil and environmental engineering. On the other hand, the authors of chapter 7, “The Contagion of Prosocial Behavior and the Emergence of Voluntary-Contribution Communities,” are from sociology and information systems departments. This makes it more like a reference book rather than a book to be read in one sitting, expecting coherent comprehension of a single topic. This is also the strength of the book, to be potentially utilized by scientists from many disciplines who are interested in data analysis and modeling.

This book is published in Springer’s “Computational Social Sciences” series. This series publishes books that cover quantitative and computational methods to explain large-scale social phenomena. More information about this series can be found on Springer’s website (http://www.springer.com/series/11784). On the back cover of the book, the editors claim: “We hope to not only provide an unifying framework to understand and characterize social phenomena, but also to help foster the dialogue between researchers working on similar problems from different fields and perspectives.” Unfortunately, I did not see a unifying framework in this book. However, the second part of their claim--“to help foster the dialogue between researchers working on similar problems”--is right on target, and that is where the value of this book rests.

The introduction, written by the editors as chapter 1, is very informative for the uninitiated in the area of data sciences. Chapter 2 essentially covers human mobility by analyzing large-scale cellphone data. Chapter 3, “Face-to-Face Interactions,” covers recent advances related to the measurement, analysis, and modeling of face-to-face interactions. Chapter 4 is “Modeling and Predicting Human Infectious Diseases.” Chapter 5, “Early Signs of Financial Market Moves Reflected by Google Searches,” is about developing insight concerning the early information-gathering stages of collective decision-making applied to financial markets. Chapter 6 covers the topic of online interactions. Chapter 7 is a difficult read. In their results, the authors suggest that “the non-rivalness of most user-generated content provides a plausible explanation for the rapid diffusion of helping behavior in online communities.” Chapter 8, the last chapter of the first part, covers exactly what its title suggests: “Understanding the Scientific Enterprise: Citation Analysis, Data and Modeling.”

The first chapter of the second part is on “Behavioral Changes and Adaptation Induced by Epidemics.” Chapter 10, “Uncovering Criminal Behavior with Computational Tools,” is fun reading. It explores an in-depth study of criminal behavior and dynamics with social network analysis techniques. Chapter 11, “Modeling Human Conflict and Terrorism Across Geographic Scales,” includes a short appendix on the details of their model. Chapter 12, the last chapter of the book, addresses “Event-Related Crowd Activities on Social Media.” Each chapter includes references, and the book ends with a brief index.

In conclusion, this book is essentially a reference book on data analysis and models. It does a superb job in this domain. I believe that the claim of fostering dialogue among researchers from different fields and perspectives is largely achieved.