Doing Computational Social Science

John McLevey is an Associate Professor in the Department of Knowledge Integration at the University of Waterloo (ON, Canada). He is also appointed to the Departments of Sociology & Legal Studies and Geography and Environmental Management, is a Policy Fellow at the Balsillie School of International Affairs, and a Member of the Cybersecurity and Privacy Institute at the University of Waterloo. His work is funded by research grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation. His current research project focuses on disinformation, censorship, and political deliberation in the public sphere across a wide variety of national contexts and political regimes. He wrote Doing Computational Social Science (SAGE Publishing, 2021) from his experiences as a researcher and advisor, as well as teaching courses in computational social science, data science, and research methods to students from diverse disciplinary backgrounds at the undergraduate and graduate level.

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