Sociological Foundations of Computational Social Science (Translational Systems Sciences, 40)

Preface
Acknowledgement
Contents
Chapter 1: Introduction
	1.1 Backdrop and Purpose of the Book
	1.2 Organization of the Book
	References
Chapter 2: Sociological Foundations of Computational Social Science
	2.1 Introduction: Computational Social Science and Sociology
	2.2 Strength and Problems of Computational Social Science
	2.3 How Can We Fill the Gap?
		2.3.1 Agent-Based Modeling and Interpretation
		2.3.2 Digital Data Analysis and Sociology
	2.4 Conclusions: Toward Productive Collaboration Between Sociology and Computational Social Science
	References
Chapter 3: Methodological Contributions of Computational Social Science to Sociology
	3.1 Introduction
	3.2 Machine Learning
		3.2.1 The Culture and Logic of Machine Learning
		3.2.2 Definition of Machine Learning
		3.2.3 Prediction and Supervised Machine Learning
			3.2.3.1 Sample Splitting
			3.2.3.2 Bias-Variance Tradeoff
			3.2.3.3 Three-Way Sample Splitting and K-Fold Cross-Validation
			3.2.3.4 Regularization
		3.2.4 Measurement, Discovery, and Unsupervised Machine Learning
	3.3 Potential of Machine Learning
		3.3.1 Breaking Away from the Deductive Model
		3.3.2 The Machine Learning Paradigm as a Normative Epistemic Model
		3.3.3 Machine Learning Model as a Human Decision and Cognitive Model
		3.3.4 Challenges for Machine Learning
	3.4 Conventional Methods of Statistical Analysis in Sociology
		3.4.1 Aims of Quantitative Sociological Studies
		3.4.2 Description
		3.4.3 Prediction
		3.4.4 Causal Inference
		3.4.5 Problems of the Conventional Regression Model
	3.5 Machine Learning in Sociology
		3.5.1 Application of the Prediction Framework
		3.5.2 Automatic Coding
	3.6 Toward Further Applications in Sociology
		3.6.1 Heterogeneity of Causal Effects
		3.6.2 Scientific Regret Minimization Method
	3.7 Conclusion
	References
Chapter 4: Computational Social Science: A Complex Contagion
	4.1 The First Wave
	4.2 Agent-Based Modeling
	4.3 Social Contagion
	4.4 From Latte Liberals to Naked Emperors
	4.5 The Second Wave
	4.6 Structural Holes and Network Wormholes
	4.7 Conclusion
	References
Chapter 5: Model of Meaning
	5.1 Introduction
	5.2 Theories of Meaning in Sociology
		5.2.1 Weber´s Social Action
		5.2.2 Schutz´s Phenomenological Sociology
		5.2.3 Bourdieu´s Cognitive Sociology
		5.2.4 White and DiMaggio´s Sociology of Culture
		5.2.5 In Summary: Semantic Models in Cultural Sociology Today
	5.3 Preparatory Considerations on the Semantic Model
		5.3.1 Function of Meaning: Prediction
		5.3.2 Relationality of Meaning
		5.3.3 Semantic Learning
	5.4 Computational Linguistics Model
		5.4.1 Topic Model
		5.4.2 Word-Embedding Model
		5.4.3 Topic Models and Word-Embedding Models
	5.5 Language Model and Explanation of Actions
	5.6 Conclusion
	References
Chapter 6: Sociological Meaning of Contagion
	6.1 Contagion as a Main Theme in Sociology
	6.2 Complex Contagion
	6.3 Role of Meaning and Interpretation in the Contagion Process
	6.4 Big Data Analysis of Diffusions, Interpretation, and Meaning
	6.5 Conclusion
	References
Chapter 7: Polarization of Opinion
	7.1 Background
		7.1.1 The Concept of Opinion Polarization
	7.2 The Mechanism of Opinion Polarization
		7.2.1 Computational Social Science and Opinion Polarization
	7.3 New Methodology and Data for Opinion Polarization Research
		7.3.1 Network Analysis
		7.3.2 Natural Language Processing
	7.4 Digital Experiment
	7.5 Discussion
	References
Chapter 8: Coda
	8.1 Revisiting the Relationship Between Computational Social Science and Sociology
	8.2 Beyond the Deductive Approach
	8.3 A New Way of Using Computational Linguistic Models in Sociology
	8.4 What Is the Next Step?
	References