Computational approaches offer exciting opportunities for us to do social science differently. This beginnerâ s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research.
Computational approaches offer exciting opportunities for us to do social science differently. This beginnerâ s guide discusses a range of computational methods and how to use them to study the problems and questions you want to research.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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