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Doing Computational Social Science
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Doing Computational Social Science
A Practical Introduction

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April 2022 | 688 pages | SAGE Publications Ltd

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.


 
Introduction: Learning to do computational social science
 
Part I: Foundations
 
Chapter 1: Setting up your open source scientific computing environment
 
Chapter 2: Python programming: The basics
 
Chapter 3: Python programming: Data structures, functions and files
 
Chapter 4: Collecting data from Application Programming Interfaces (APIs)
 
Chapter 5: Collecting data from the web: Scraping
 
Chapter 6: Processing structured data
 
Chapter 7: Visualisation and exploratory data analysis
 
Chapter 8: Latent factors and components
 
Part II: Fundamentals of text analysis
 
Chapter 9: Processing natural language data
 
Chapter 10: Iterative text analysis
 
Chapter 11: Exploratory text analysis
 
Chapter 12: Text similarity and latent semantic space
 
Part III: Fundamentals of network analysis
 
Chapter 13: Social networks and relational thinking
 
Chapter 14: Connection and clustering in social networks
 
Chapter 15: Influence, inequality and power in social networks
 
Chapter 16: Going viral: Modelling the epidemic spread of simple contagions
 
Chapter 17: Not so fast: Modelling the diffusion of complex contagions
 
Part IV: Research ethics and machine learning
 
Chapter 18: Research ethics, politics and practices
 
Chapter 19: Machine learning: Symbolic and connectionist
 
Chapter 20: Supervised learning with regression and cross-validation
 
Chapter 21: Supervised learning with tree-based models
 
Chapter 22: Neural networks and deep learning
 
Chapter 23: Developing neural network models with Keras and Tensorflow
 
Part V: Bayesian machine learning and probabilistic programming
 
Chapter 24: Statistical machine learning and generative models
 
Chapter 25: Probability: A primer
 
Chapter 26: Approximate posterior inference with stochastic sampling and MCMC
 
Part VI: Bayesian data analysis and latent variable modelling with relational and text data
 
Chapter 27: Bayesian regression models with probabilistic programming
 
Chapter 28: Bayesian hierarchical regression modelling
 
Chapter 29: Variational Bayes and the craft of generative topic modelling
 
Chapter 30: Generative network analysis with Bayesian stochastic blockmodels
 
Part VII: Embeddings, transformer models and named entity recognition
 
Chapter 31: Can we model meaning?: Contextual representation and neural word embeddings
 
Chapter 32: Named entity recognition, transfer learning and transformer models

This book adds two key components critical for social science students that are often lacking in other texts. The first is a discussion of key computing components starting at the most basic – often these steps are overlooked in texts written for computer science students. Second is the excellent grounding in and integration with social science theory and concepts.

Lorien Jasny
University of Exeter

McLevey has provided us with a book that clearly imparts the technical skills of computational methods, but crucially he has done so in a way that accessibly embeds them in a social science context. Whilst we’ve previously been told “how” to do computational social science, McLevey expertly ensures we understand “why” too.

James Allen-Robertson
Senior Lecturer / Computational Sociologist, Department of Sociology, University of Essex

The McLevey book covers modern machine learning style research for both information science and computing students. For other social science students who have programming skills, or are at a graduate level, then this book might also be a good resource with plenty of useful information. However it is written using a handbook style and lacking an academic thrust. It is very practical and exercise based. Students would still need the Oates book to help them writeup.

Dr Steve McKeever
Department of Informatics & Media, Uppsala University
August 8, 2022

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