Doing computational social science : a practical introduction / John McLevey.

Format:

Book

Author/Creator:

McLevey, John, author.

Series:

Core textbook

Language:

English

Subjects (All):

Research methods & evaluation.

Physical Description:

1 online resource (768 pages).

Edition:

1st ed.

Place of Publication:

Washington, D.C. : SAGE Publications Ltd, 2021.

Summary:

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, including machine learning and social network analysis, 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. Teaches you good habits and working practices that enable you to do programming well. This book is for anyone who wants to use computational methods to conduct a social science research project. Supported by a wealth of online resources, including video tutorials and datasets for practice so you can learn at your own pace, this book equips you with the skills to conduct computational social science research for the first time, with confidence.

Contents:

DOING COMPUTATIONAL SOCIAL SCIENCE - FRONT COVER

DOING COMPUTATIONAL SOCIAL SCIENCE

COPYRIGHT

CONTENTS

DISCOVER YOUR ONLINE RESOURCES!

ACKNOWLEDGEMENTS

ABOUT THE AUTHOR

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

CHAPTER - 5 COLLECTING DATA FROM THE WEB: SCRAPING

CHAPTER - 6 PROCESSING STRUCTURED DATA

CHAPTER - 7 VISUALIZATION 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 - WORKING WITH WORD FREQUENCIES AND PROPORTIONS

CHAPTER - 12 EXPLORATORY TEXT ANALYSIS - WORD WEIGHTS, TEXT SIMILARITY, AND LATENT SEMANTIC ANALYSIS

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 DATA ANALYSIS AND GENERATIVE MODELLING WITH 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 PROBABILISTIC PROGRAMMING AND BAYESIAN LATENT VARIABLE MODELS FOR STRUCTURED, 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 BLOCK MODELS

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

REFERENCES

INDEX.

Notes:

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ISBN:

9781036212889

1036212882

9781529738490

1529738490

9781529736700

1529736706

OCLC:

1285611738