Wouter van Atteveldt, Damian Trilling, Carlos Arcila Calderon

Computational Analysis of Communication

• Broschiertes Buch

Produktbeschreibung

Provides clear guidance on leveraging computational techniques to answer social science questions

In disciplines such as political science, sociology, psychology, and media studies, the use of computational analysis is rapidly increasing. Statistical modeling, machine learning, and other computational techniques are revolutionizing the way electoral results are predicted, social sentiment is measured, consumer interest is evaluated, and much more. Computational Analysis of Communication teaches social science students and practitioners how computational methods can be used in a broad range of applications, providing discipline-relevant examples, clear explanations, and practical guidance.

Assuming little or no background in data science or computer linguistics, this accessible textbook teaches readers how to use state-of-the art computational methods to perform data-driven analyses of social science issues. A cross-disciplinary team of authors--with expertise in both the social sciences and computer science--explains how to gather and clean data, manage textual, audio-visual, and network data, conduct statistical and quantitative analysis, and interpret, summarize, and visualize the results. Offered in a unique hybrid format that integrates print, ebook, and open-access online viewing, this innovative resource:
_ Covers the essential skills for social sciences courses on big data, data visualization, text analysis, predictive analytics, and others
_ Integrates theory, methods, and tools to provide unified approach to the subject
_ Includes sample code in Python and links to actual research questions and cases from social science and communication studies
_ Discusses ethical and normative issues relevant to privacy, data ownership, and reproducible social science
_ Developed in partnership with the International Communication Association and by the editors of Computational Communication Research

Computational Analysisof Communication is an invaluable textbook and reference for students taking computational methods courses in social sciences, and for professional social scientists looking to incorporate computational methods into their work.

Produktdetails

• Verlag: Wiley & Sons / Wiley-Blackwell
• Artikelnr. des Verlages: 1A119680230
• 1. Auflage
• Seitenzahl: 336
• Erscheinungstermin: 3. März 2022
• Englisch
• Abmessung: 253mm x 177mm x 21mm
• Gewicht: 628g
• ISBN-13: 9781119680239
• ISBN-10: 1119680239
• Artikelnr.: 61316754

Autorenporträt

Dr. Wouter van Atteveldt is an Associate Professor of Political Communication at Vrije Universiteit, Amsterdam. He is co-founder of the Computational Methods division of the International Communication Association, and Founding Chief Editor of Computational Communication Research. He has published extensively on innovative methods for analyzing political text and contributed to a number of relevant R and Python packages. Dr. Damian Trilling is an Associate Professor, Department of Communication Science, at the University of Amsterdam, and Associate Editor of Computational Communication Research. His research uses computational methods such as the analysis of digital trace data and large-scale text analysis to study the use and effects of news media. He has developed extensive teaching materials to introduce social scientists to the Python programming language. Dr. Carlos Arcila Calderón is an Associate Professor, Department of Sociology and Communication, at the University of Salamanca, Chief Editor of the journal Disertaciones, and member of the Editorial Board of Computational Communication Research. He has published extensively on new media and social media studies, and has led the prototype Autocop, a Spark-based environment to run distributed supervised sentiment analysis of Twitter messages.

Inhaltsangabe

Preface xi

Acknowledgments xiii

1 Introduction 1

1.1 The Role of Computational Analysis in the Social Sciences 1

1.2 Why Python and/or R? 3

1.3 How to Use This Book 4

1.4 Installing R and Python 5

1.4.1 Installing R and RStudio 7

1.4.2 Installing Python and Jupyter Notebook 9

1.5 Installing Third-Party Packages 12

2 Getting Started: Fun with Data and Visualizations 13

2.1 Fun With Tweets 14

2.2 Fun With Textual Data 15

2.3 Fun With Visualizing Geographic Information 17

2.4 Fun With Networks 19

3 Programming Concepts for Data Analysis 23

3.1 About Objects and Data Types 24

3.1.1 Storing Single Values: Integers, Floating-Point Numbers, Booleans 25

3.1.2 Storing Text 26

3.1.3 Combining Multiple Values: Lists, Vectors, And Friends 28

3.1.4 Dictionaries 32

3.1.5 From One to More Dimensions: Matrices and n-Dimensional Arrays 33

3.1.6 Making Life Easier: Data Frames 34

3.2 Simple Control Structures: Loops and Conditions 35

3.2.1 Loops 36

3.2.2 Conditional Statements 37

3.3 Functions and Methods 39

4 How to Write Code 43

4.1 Re-using Code: How Not to Re-Invent the Wheel 43

4.2 Understanding Errors and Getting Help 46

4.2.1 Error Messages 46

4.2.2 Debugging Strategies 48

4.3 Best Practice: Beautiful Code, GitHub, and Notebooks 49

5 From File to Data Frame and Back 55

5.1 Why and When Do We Use Data Frames? 56

5.2 Reading and Saving Data 57

5.2.1 The Role of Files 57

5.2.2 Encodings and Dialects 59

5.2.3 File Handling Beyond Data Frames 61

5.3 Data from Online Sources 62

6 Data Wrangling 65

6.1 Filtering, Selecting, and Renaming 66

6.2 Calculating Values 67

6.3 Grouping and Aggregating 69

6.3.1 Combining Multiple Operations 70

6.3.2 Adding Summary Values 71

6.4 Merging Data 72

6.4.1 Equal Units of Analysis 72

6.4.2 Inner and Outer Joins 75

6.4.3 Nested Data 76

6.5 Reshaping Data: Wide To Long And Long To Wide 78

6.6 Restructuring Messy Data 79

7 Exploratory Data Analysis 83

7.1 Simple Exploratory Data Analysis 84

7.2 Visualizing Data 87

7.2.1 Plotting Frequencies and Distributions 88

7.2.2 Plotting Relationships 92

7.2.3 Plotting Geospatial Data 98

7.2.4 Other Possibilities 99

7.3 Clustering and Dimensionality Reduction 100

7.3.1 k-means Clustering 101

7.3.2 Hierarchical Clustering 102

7.3.3 Principal Component Analysis and Singular Value Decomposition 106

8 Statistical Modeling and Supervised Machine Learning 113

8.1 Statistical Modeling and Prediction 115

8.2 Concepts and Principles 117

8.3 Classical Machine Learning: From Naïve Bayes to Neural Networks 122

8.3.1 Naïve Bayes 122

8.3.2 Logistic Regression 124

8.3.3 Support Vector Machines 125

8.3.4 Decision Trees and Random Forests 127

8.3.5 Neural Networks 129

8.4 Deep Learning 130

8.4.1 Convolutional Neural Networks 131

8.5 Validation and Best Practices 133

8.5.1 Finding a Balance Between Precision and Recall 133

8.5.2 Train, Validate, Test 137

8.5.3 Cross-validation and Grid Search 138

9 Processing Text 141

9.1 Text as a String of Characters 142

9.1.1 Methods for