Cultural Analytics

by Lev Manovich

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 terabytes of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Sound Cloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data, with a particular focus on visual media. Drawing on more than a decade of research and projects from his own lab, Manovich—the founder of the field of cultural analytics—offers a gentle, nontechnical introduction to selected key concepts of data science and discusses the ways that our society uses data and algorithms.

Manovich offers examples of computational cultural analysis and discusses the shift from “new media” to “more media”; explains how to turn cultural processes into computational data; and introduces concepts for exploring cultural datasets using data visualization as well as other recently developed methods for analyzing image and video datasets. He considers both the possibilities and the limitations of computational methods, and how using them challenges our existing ideas about culture and how to study it.

Cultural Analytics is a book of media theory. Arguing that before we can theorize digital culture, we need to see it, and that, because of its scale, to see it we need computers, Manovich provides scholars with practical tools for studying contemporary media.

Genres: Nonfiction, Technology

336 pages, Hardcover

Published October 20, 2020

About the author

Lev Manovich is an artist, an author and a theorist of digital culture. He is a Distinguished Professor at the Graduate Center of the City University of New York. Manovich played a key role in creating four new research fields: new media studies (1991-), software studies (2001-), cultural analytics (2007-) and AI aesthetics (2018-). Manovich's current research focuses on generative media, AI culture, digital art, and media theory.
Manovich is the founder and director of the Cultural Analytics Lab (called Software Studies Initiative 2007-2016), which pioneered use of data science and data visualization for the analysis of massive collections of images and video (cultural analytics). The lab was commissioned to create visualizations of cultural datasets for Google, New York Public Library, and New York's Museum of Modern Art (MoMA).
He is the author and editor of 15 books including The Language of New Media that has been translated into fourteen languages. Manovich's latest academic book Cultural Analytics was published in 2020 by the MIT Press.

Reviews

[Alexander Smith · Rating 4 out of 5]

This is an extended outline of how Lev Manovich would explain the theory and methodological approach of what he calls "Cultural Analytics." It doesn't actually explain how to do the methods, but rather it offers a language of how to approach particular kinds of cultural questions as they relate to data science-like methods. He covers everything from ways data can be organized to ways to visualize that data to the questions a variety of data structures can approach.

All in all, this book is great introduction for a humanities student who wishes to understand more of a social/information/data science approach to their research. Also it's a good summary for a quantitative social/computer science scholar who wishes to understand questions a media humanities scholar might approach.

That said, I felt as though much of this was an overview of things many researchers will already know after they've reached their grad program.

[Carter · Rating 4 out of 5]

This book is of interest to me, because I was curious about some of the trends in visualization, when it comes to data science, and how it has impacted the notion of the infographic. There are very good old books, on this topic (Minard for example), the question is what here, makes good quality work? And how have the parameters changed, with new requirements and needs? Data science will likely transform the field, and it is interesting to see new ideas as they emerge.

[Ismael Lazreg M · Rating 2 out of 5]

como bad bunny: ni bien ni mal

[Federica Malpasso · Rating 3 out of 5]

non male, scorrevole