Doing Computational Social Science A Practical Introduction

✍ Scribed by John McLevey

• Publisher: SAGE Publications Ltd
• Year: 2022
• Tongue: English
• Leaves: 897
• Edition: 1
• Category: Library

✦ Table of Contents

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Acknowledgements
About the Author
Introduction: Learning to Do Computational Social Science
0.1 Who Is This Book For?
0.2 Roadmap
0.3 Datasets Used in This Book
0.4 Learning Materials
0.5 Conclusion
Part I Foundations
1 Setting Up Your Open Source Scientific Computing Environment
1.1 Learning Objectives
1.2 Introduction
1.3 Command Line Computing
1.4 Open Source Software
1.5 Version Control Tools
1.6 Virtualization Tools
1.7 Putting the Pieces Together: Python, Jupyter, conda, and git
1.8 Conclusion
2 Python Programming: The Basics
2.1 Learning Objectives
2.2 Learning Materials
2.3 Introduction
2.4 Learning Python
2.5 Python Foundations
2.6 Conclusion
3 Python Programming: Data Structures, Functions, and Files
3.1 Learning Objectives
3.2 Learning Materials
3.3 Introduction
3.4 Working With Python’s Data Structures
3.5 Custom Functions
3.6 Reading and Writing Files
3.7 Pace Yourself
3.8 Conclusion
4 Collecting Data From Application Programming Interfaces
4.1 Learning Objectives
4.2 Learning Materials
4.3 Introduction
4.4 What Is an API?
4.5 Getting Practical: Working With APIs
4.6 Conclusion
5 Collecting Data From the Web: Scraping
5.1 Learning Objectives
5.2 Learning Materials
5.3 Introduction
5.4 An HTML and CSS Primer for Web Scrapers
5.5 Developing Your First Web Scraper
5.6 Ethical and Legal Issues in Web Scraping
5.7 Conclusion
6 Processing Structured Data
6.1 Learning Objectives
6.2 Learning Materials
6.3 Introduction
6.4 Practical Pandas: First Steps
6.5 Understanding Pandas Data Structures
6.6 Aggregation and Grouped Operations
6.7 Working With Time-Series Data
6.8 Combining Dataframes
6.9 Conclusion
7 Visualization and Exploratory Data Analysis
7.1 Learning Objectives
7.2 Learning Materials
7.3 Introduction
7.4 Iterative Research Workflows: EDA and Box’s Loop
7.5 Effective Visualization
7.6 Univariate EDA: Describing and Visualizing Distributions
7.7 Multivariate EDA
7.8 Conclusion
8 Latent Factors and Components
8.1 Learning Objectives
8.2 Learning Materials
8.3 Introduction
8.4 Latent Variables and the Curse of Dimensionality
8.5 Conducting a Principal Component Analysis in Sklearn
8.6 Conclusion
Part II Fundamentals of Text Analysis
9 Processing Natural Language Data
9.1 Learning Objectives
9.2 Learning Materials
9.3 Introduction
9.4 Text Processing
9.5 Normalizing Text via Lemmatization
9.6 Part-of-Speech Tagging
9.7 Syntactic Dependency Parsing
9.8 Conclusion
10 Iterative Text Analysis
10.1 Learning Objectives
10.2 Learning Materials
10.3 Introduction
10.4 Exploration in Context: Text Analysis Pipelines
10.5 Count-Based Feature Extraction: From Strings to a Bag of Words
10.6 Close Reading
10.7 Conclusion
11 Exploratory Text Analysis – Working With Word Frequencies and Proportions
11.1 Learning Objectives
11.2 Learning Materials
11.3 Introduction
11.4 Scaling Up: Processing Political Speeches
11.5 Creating DTMs With Sklearn
11.6 Conclusion
12 Exploratory Text Analysis – Word Weights, Text Similarity, and Latent Semantic Analysis
12.1 Learning Objectives
12.2 Learning Materials
12.3 Introduction
12.4 Exploring Latent Semantic Space With Matrix Decomposition
12.5 Conclusion
Part III Fundamentals of Network Analysis
13 Social Networks and Relational Thinking
13.1 Learning Objectives
13.2 Learning Materials
13.3 Introduction
13.4 What Are Social Networks?
13.5 Working With Relational Data
13.6 Walk Structure and Network Flow
13.7 Conclusion
14 Connection and Clustering in Social Networks
14.1 Learning Objectives
14.2 Learning Materials
14.3 Introduction
14.4 Micro-Level Network Structure and Processes
14.5 Detecting Cohesive Subgroups and Assortative Structure
14.6 Conclusion
15 Influence, Inequality, and Power in Social Networks
15.1 Learning Objectives
15.2 Learning Materials
15.3 Introduction
15.4 Centrality Measures: The Big Picture
15.5 Shortest Paths and Network Flow
15.6 Betweenness Centrality, Two Ways
15.7 Popularity, Power, and Influence
15.8 Conclusion
15.9 Chapter Appendix
16 Going Viral: Modelling the Epidemic Spread of Simple Contagions
16.1 Learning Objectives
16.2 Learning Materials
16.3 Introduction
16.4 Epidemic Spread and Diffusion
16.5 Modelling Spreading Processes With NDlib
16.6 Simple Contagions and Epidemic Spread
16.7 Conclusion
17 Not So Fast: Modelling the Diffusion of Complex Contagions
17.1 Learning Objectives
17.2 Learning Materials
17.3 Introduction
17.4 From Simple to Complex Contagions
17.5 Beyond Local Neighbourhoods: Network Effects and Thresholds
17.6 Threshold Models for Complex Contagions
17.7 Conclusion
Part IV Research Ethics and Machine Learning
18 Research Ethics, Politics, and Practices
18.1 Learning Objectives
18.2 Learning Materials
18.3 Introduction
18.4 Research Ethics and Social Network Analysis
18.5 Informed Consent, Privacy, and Transparency
18.6 Bias and Algorithmic Decision-Making
18.7 Ditching the Value-Free Ideal for Ethics, Politics, and Science
18.8 Conclusion
19 Machine Learning: Symbolic and Connectionist
19.1 Learning Objectives
19.2 Learning Materials
19.3 Introduction
19.4 Types of Machine Learning
19.5 Symbolic and Connectionist Machine Learning
19.6 Conclusion
20 Supervised Learning With Regression and Cross-validation
20.1 Learning Objectives
20.2 Learning Materials
20.3 Introduction
20.4 Supervised Learning With Linear Regression
20.5 Classification With Logistic Regression
20.6 Conclusion
21 Supervised Learning With Tree-Based Models
21.1 Learning Objectives
21.2 Learning Materials
21.3 Introduction
21.4 Rules-Based Learning With Trees
21.5 Ensemble Learning
21.6 Evaluation Beyond Accuracy
21.7 Conclusion
22 Neural Networks and Deep Learning
22.1 Learning Objectives
22.2 Learning Materials
22.3 Introduction
22.4 The Perceptron
22.5 Multilayer Perceptrons
22.6 Training ANNs With Backpropagation and Gradient Descent
22.7 More Complex ANN Architectures
22.8 Conclusion
23 Developing Neural Network Models With Keras and TensorFlow
23.1 Learning Objectives
23.2 Learning Materials
23.3 Introduction
23.4 Getting Started With Keras
23.5 End-to-End Neural Network Modelling
23.6 Conclusion
Part V Bayesian Data Analysis and Generative Modelling with Probabilistic Programming
24 Statistical Machine Learning and Generative Models
24.1 Learning Objectives
24.2 Learning Materials
24.3 Introduction
24.4 Statistics, Machine Learning, and Statistical Machine Learning: Where Are the Boundaries and What Do They Bind?
24.5 Generative Versus Discriminative Models
24.6 Conclusion
25 Probability: A Primer
25.1 Learning Objectives
25.2 Learning Materials
25.3 Introduction
25.4 Foundational Concepts in Probability Theory
25.5 Probability Distributions and Likelihood Functions
25.6 Continuous Distributions, Probability Density Functions
25.7 Joint and Conditional Probabilities
25.8 Bayesian Inference
25.9 Posterior Probability
25.10 Conclusion
26 Approximate Posterior Inference With Stochastic Sampling and MCMC
26.1 Learning Objectives
26.2 Learning Materials
26.3 Introduction
26.4 Bayesian Regression
26.5 Stochastic Sampling Methods
26.6 Conclusion
Part VI Probabilistic Programming and Bayesian Latent Variable Models for Structured, Relational, and Text Data
27 Bayesian Regression Models With Probabilistic Programming
27.1 Learning Objectives
27.2 Learning Materials
27.3 Introduction
27.4 Developing Our Bayesian Model
27.5 Conclusion
28 Bayesian Hierarchical Regression Modelling
28.1 Learning Objectives
28.2 Learning Materials
28.3 Introduction
28.4 So, What’s a Hierarchical Model?
28.5 Goldilocks and the Three Pools
28.6 The Best Model Our Data Can Buy
28.7 The Fault in Our (Lack of) Stars
28.8 Conclusion
29 Variational Bayes and the Craft of Generative Topic Modelling
29.1 Learning Objectives
29.2 Learning Materials
29.3 Introduction
29.4 Generative Topic Models
29.5 Topic Modelling With Gensim
29.6 Conclusion
30 Generative Network Analysis With Bayesian Stochastic Block Models
30.1 Learning Objectives
30.2 Learning Materials
30.3 Introduction
30.4 Block Modelling With Graph-Tool
30.5 Conclusion
Part VII Embeddings, Transformer Models, and Named Entity Recognition
31 Can We Model Meaning? Contextual Representation and Neural Word Embeddings
31.1 Learning Objectives
31.2 Learning Materials
31.3 Introduction
31.4 What Words Mean
31.5 What Are Neural Word Embeddings?
31.6 Cultural Cartography: Getting a Feel for Vector Space
31.7 Learning Embeddings With Gensim
31.8 Comparing Embeddings
31.9 Conclusion
32 Named Entity Recognition, Transfer Learning, and Transformer Models
32.1 Learning Objectives
32.2 Learning Materials
32.3 Introduction
32.4 Named Entity Recognition
32.5 Transformer Models
32.6 Conclusion
References
Index