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October 2020, Week 4

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Subject:
From:
Julian Sunley <[log in to unmask]>
Reply To:
Film and TV Studies Discussion List <[log in to unmask]>
Date:
Wed, 21 Oct 2020 13:40:32 +0100
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We are pleased to announce the release of Lev Manovich’s new book Cultural
Analytics, published by The MIT Press: h
<https://mitpress.mit.edu/books/cultural-analytics>
ttp://manovich.net/index.php/projects/cultural-analytics
<http://manovich.net/index.php/projects/cultural-analytics>. The book is
available in hardback or as an e-book.

How can we see a billion images? In Cultural Analytics, Lev Manovich
presents the principles and methods required to computationally analyze
contemporary culture on a massive scale. One of the book's innovations is
showing how these methods can be applied to visual media.

Lev Manovich <http://manovich.net/index.php/about> is a Presidential
Professor at City University of New York (CUNY), and director of the
Cultural Analytics Lab. Based on 13 years of research in this lab, Cultural
Analytics is his fifteenth book.

Kind regards,

Julian Sunley

Cultural Analytics Lab

[log in to unmask]

-------------------

Description of Cultural Analytics:

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 SoundCloud, 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.

Endorsements:

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

“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 at Ludwig Maximilian
University of Munich

Table of Contents:

Introduction: How to See One Billion Images

I Studying Culture at Scale

1 From New Media to More Media

2 The Science of Culture?

3 Culture Industry and Media Analytics

II Representing Culture as Data

4 Types of Cultural Data

5 Cultural Sampling

6 Metadata and Features

7 Language, Categories, and Senses

III Exploring Cultural Data

8 Information Visualization

9 Exploratory Media Analysis

10 Methods of Media Visualization

Conclusion: Can We Think without Categories?

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