Doing Computational Social Science

A Practical Introduction

John McLevey

• Publisher: SAGE Publications Ltd
• Year: 2021
• Print ISBN: 9781526468185, 9781526468192
• eBook ISBN: 9781529737592
• Edition: 1
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

Computational approaches offer exciting opportunities for us to do social science differently. This beginner's guide discusses a range of computational methods and how to use them to study the problems and questions you want to research.

It assumes no knowledge of programming, offering step-by-step guidance for coding in Python and drawing on examples of real data analysis to demonstrate how you can apply each approach in any discipline.

The book also:

• Considers important principles of social scientific computing, including transparency, accountability and reproducibility.
• Understands the realities of completing research projects and offers advice for dealing with issues such as messy or incomplete data and systematic biases.
• Empowers you to learn at your own pace, with online resources including screencast tutorials and datasets that enable you to practice your skills and get up to speed.

For anyone who wants to use computational methods to conduct a social science research project, this book equips you with the skills, good habits and best working practices to do rigorous, high quality work.

Table of contents

• Cover
• Half Title
• Publisher Note
• Title Page
• Copyright Page
• Contents
• Discover your online resources!
• Acknowledgements
• About the Author
• Introduction: Learning to Do Computational Social Science
• Part I Foundations
• 1 Setting Up Your Open Source Scientific Computing Environment
• 2 Python Programming: The Basics
• 3 Python Programming: Data Structures, Functions, and Files
• 4 Collecting Data From Application Programming Interfaces
• 5 Collecting Data From the Web: Scraping
• 6 Processing Structured Data
• 7 Visualization and Exploratory Data Analysis
• 8 Latent Factors And Components
• Part II Fundamentals of Text Analysis
• 9 Processing Natural Language Data
• 10 Iterative Text Analysis
• 11 Exploratory Text Analysis – Working With Word Frequencies And Proportions
• 12 Exploratory Text Analysis – Word Weights, Text Similarity, and Latent Semantic Analysis
• Part III Fundamentals of Network Analysis
• 13 Social Networks and Relational Thinking
• 14 Connection and Clustering in Social Networks
• 15 Influence, Inequality, and Power in Social Networks
• 16 Going Viral: Modelling the Epidemic Spread of Simple Contagions
• 17 Not So Fast: Modelling the Diffusion of Complex Contagions
• Part IV Research Ethics and Machine Learning
• 18 Research Ethics, Politics, and Practices
• 19 Machine Learning: Symbolic and Connectionist
• 20 Supervised Learning With Regression and Cross-Validation
• 21 Supervised Learning With Tree-Based Models
• 22 Neural Networks and Deep Learning
• 23 Developing Neural Network Models With Keras and TensorFlow
• Part V Bayesian Data Analysis and Generative Modelling with Probabilistic Programming
• 24 Statistical Machine Learning and Generative Models
• 25 Probability: A Primer
• 26 Approximate Posterior Inference With Stochastic Sampling and MCMC
• Part VI Probabilistic Programming and Bayesian Latent Variable Models for Structured, Relational, and Text Data
• 27 Bayesian Regression Models With Probabilistic Programming
• 28 Bayesian Hierarchical Regression Modelling
• 29 Variational Bayes and the Craft of Generative Topic Modelling
• 30 Generative Network Analysis With Bayesian Stochastic Block Models
• Part VII Embeddings, Transformer Models, and Named Entity Recognition
• 31 Can We Model Meaning? Contextual Representation and Neural Word Embeddings
• 32 Named Entity Recognition, Transfer Learning, and Transformer Models
• References
• Index