Cultural Analytics

by Lev Manovich

Overview

A book at the intersection of data science and media studies, presenting concepts and methods for computational analysis of cultural data.

How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture—the billions of photographs shared on social media  every day, the hundreds of millions of songs created by twenty million musicians on Soundcloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data. Drawing on more than a decade of research and projects from his own lab, Manovich offers a gentle, nontechnical introduction to the core ideas of data analytics and discusses the ways that our society uses data and algorithms.

Product Details

ISBN-13:	9780262037105
Publisher:	MIT Press
Publication date:	10/20/2020
Pages:	336
Sales rank:	1,131,686
Product dimensions:	7.20(w) x 9.10(h) x 1.10(d)

About the Author

Lev Manovich is Professor of Visual Arts, University of California, San Diego. His book The Language of New Media (MIT Press, 2001) has been hailed as "the most suggestive and broad ranging media history since Marshall McLuhan."

Table of Contents

Acknowledgments ix

Illustrations xi

Introduction: How to See One Billion Images 1

Looking at Culture with Computers 2

Cultural Analytics: Five Ideas 7

Cultural Analytics: Twelve Research Challenges 13

What Cultural Analytics Is Not 16

Cultural Analytics, Media Theory, and Software Studies 17

Using This Book in Classes 19

I Studying Culture at Scale 23

1 From New Media to More Media 25

"From New Media to More Media" (2008) 28

Observing Global Culture in Real Time 31

Cultural Analytics in Historical Context 35

2 The Science of Culture? 39

Analyzing, Visualizing, and Interacting with Cultural Data: Examples 39

History versus Present, Professionals versus Amateurs 44

The Regular versus the Particular 47

The Science of Culture? Deterministic Laws, Statistical Models, Simulation 49

3 Culture Industry and Media Analytics 53

A New Stage in Media Technology History 56

Media Analytics Examples 56

The Two Parts of Media Analytics 59

Automation: Media Analysis 61

Automation: Media Actions 64

Media Analytics and Cultural Analytics 67

II Representing Culture as Data 73

4 Types of Cultural Data 75

Media: Social Networks and Professional Networks 77

Behavior: Digital and Physical Traces 82

Representing Interaction 87

Events, Places, Organizations 89

5 Cultural Sampling 93

The Islands and the Ocean 94

Museums versus Libraries 98

Creating Representative Samples 100

How to See the Invisible 102

The Limitations of Random Samples 108

Statistics as Reduction 111

Why We Need Big Data to Study Cultures 114

Is Sampling Necessary? 116

6 Metadata and Features 121

From a World to a Dataset 122

Metadata and Features 125

Data - Objects + Features 128

Statistics in the Nineteenth and Twentieth Centuries: From a Single to Multiple Variables 133

Interpretation, Explanation, Automation 136

The Semantic Gap 139

7 Language, Categories, and Senses 145

Data Types 145

Measurement Scales 148

Language and Senses 152

Senses and Numbers 157

Measuring Perceptions 160

Top-Down and Bottom-Up Analysis 163

Prescriptive Aesthetics and Modernisms 165

Analysis Examples: 776 van Gogh Paintings and One Million Manga Pages 169

More Examples: One Million Artworks and 42,571 Movies 176

The Society of Categories 180

III Exploring Cultural Data 185

8 Information Visualization 187

What Is Visualization? 189

Reduction and Space 191

Visualization without Reduction 197

Artistic Media Visualization 199

Cultural Time Series 202

Beyond Information Visualization 203

9 Exploratory Media Analysis 207

Against Search 208

The Interface 213

Image Processing and Computer Vision 215

Using Image Features for Exploratory Media Analysis 218

Seeing versus Analyzing 221

10 Methods of Media Visualization 223

Image Montage 224

Sampling versus Summarization 232

Temporal Sampling 235

Spatial Sampling 236

Remapping 239

Conclusion: Can We Think without Categories? 245

Do We Want to "Explain" Culture? 247

Is the Goal of Cultural Analytics to Study Patterns? (Yes and No) 249

How to Think without Categories 251

Learning to See at a New Scale 254

Notes 255

Index 287

What People are Saying About This

From the Publisher

“Cultural Analytics causes an inspirational mind-shift for the study of culture. Providing key terms and an easy-to-follow non-technical explanation of how to deal with cultural data, it outlines new paradigms in the analysis of data, creation of media visualizations, and eventually the study of culture. Lev Manovich, as always, is leading the way to understanding digital culture.”
— Harald Klinke, Assistant Professor of Art History, Ludwig Maximilian University of Munich; editor of International Journal for Digital Art History
 
“This is a timely, forward-looking book. It introduces computational algorithms as methods to decode the intricacies of culture. Culture is data waiting to be translated into meaningful patterns by visualization.”
— Manuel Lima, founder of VisualComplexity.com; author of The Book of Circles
 
“Manovich provides a much-needed exploration of how our growing digital world relates to the real one, and how data reflects more than just quantitative insights.”
— Nathan Yau, founder of FlowingData
 
“Data is fundamentally reshaping the way we create, consume, analyze and visualize content, reshaping our understanding of culture. Manovich, a leading theorist at the intersection of data, arts, and media studies, offers a smart and comprehensive overview of this rapidly changing landscape, documenting the emergence of cultural analytics as a new mode of inquiry.”
— Albert-László Barabási, Professor of Network Science at Northeastern University; author of Formula and Linked