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Introduction to Computational Social Science: Principles and Applications
The emerging field of computational social science (CSS) is devoted to the pursuit of interdisciplinary social science research from an information processing perspective, through the medium of advanced computing and information technologies.
This reader-friendly textbook/reference is the first work of its kind to provide a comprehensive and unified "Introduction to Computational Social Science." Four distinct methodological approaches are examined in particular detail, namely automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. The coverage of each of these approaches is supported by a discussion of the historical context and motivations, as well as by a list of recommended texts for further reading.
Topics and features: describes the scope and content of each area of CSS, covering topics on information extraction, social networks, complexity theory, and social simulations; highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools, including extracting entities from text, computing social network indices, and building an agent-based model; presents the main classes of entities, objects, and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena.
This unique, clearly-written textbook is essential reading for graduate and advanced undergraduate students planning on embarking on a course on computational social science, or wishing to refresh their knowledge of the fundamental aspects of this exciting field.
This reader-friendly textbook/reference is the first work of its kind to provide a comprehensive and unified "Introduction to Computational Social Science." Four distinct methodological approaches are examined in particular detail, namely automated social information extraction, social network analysis, social complexity theory, and social simulation modeling. The coverage of each of these approaches is supported by a discussion of the historical context and motivations, as well as by a list of recommended texts for further reading.
Topics and features: describes the scope and content of each area of CSS, covering topics on information extraction, social networks, complexity theory, and social simulations; highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools, including extracting entities from text, computing social network indices, and building an agent-based model; presents the main classes of entities, objects, and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena.
This unique, clearly-written textbook is essential reading for graduate and advanced undergraduate students planning on embarking on a course on computational social science, or wishing to refresh their knowledge of the fundamental aspects of this exciting field.
356 pages, Paperback
First published January 31, 2014
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Displaying 1 - 3 of 3 reviews
July 3, 2014
This is a very good high-level survey of the field.
May 14, 2022
The "Introduction to Computational Social Science" is a misnomer. It is not helpful as a guidebook to lead into a new subject. Instead, it is a high-level survey for people who want to get an abstract overview of the newly emerging field.
I was very disappointed by the book's approach. On the one hand, it is overladen with detailed pieces of information, and on the other side, it lacks concrete examples and how-to dos.
I could imagine that the book is valuable as a textbook associated with introductory course lectures. Every chapter has many (not so practical) "problems" in the form of multiple choices to test abstract knowledge (names, concepts, and notations) with solutions at the end of the book. But there are also many (about a hundred (!) in the later chapters) exciting open "exercises" where students could dedicate their time to intensify their understanding by studying and solving practical questions. But these tasks have no predefined answers and are – from an educational point of view – utterly useless in a stand-alone textbook without guidance and feedback.
I was very disappointed by the book's approach. On the one hand, it is overladen with detailed pieces of information, and on the other side, it lacks concrete examples and how-to dos.
I could imagine that the book is valuable as a textbook associated with introductory course lectures. Every chapter has many (not so practical) "problems" in the form of multiple choices to test abstract knowledge (names, concepts, and notations) with solutions at the end of the book. But there are also many (about a hundred (!) in the later chapters) exciting open "exercises" where students could dedicate their time to intensify their understanding by studying and solving practical questions. But these tasks have no predefined answers and are – from an educational point of view – utterly useless in a stand-alone textbook without guidance and feedback.
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July 17, 2022Not the most absorbing introductory book you can find, bu it is comprehensible and clear enough for beginners with a limited computational background (like me :))
Displaying 1 - 3 of 3 reviews