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Cultural Analytics

Cultural Analytics argues that the only way to truly understand contemporary global culture at its unprecedented scale is to apply computational methods and data science to the systematic study of cultural artifacts, behaviors, and trends.

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What it’s about

In a world where billions of people create and share digital media every day, our traditional humanistic methods for studying culture are hopelessly inadequate. Lev Manovich, founder of the Cultural Analytics Lab, makes the case that data science, visualization, and machine learning are not just useful tools but necessary ones for anyone who wants to understand culture in the twenty-first century. Drawing on over a decade of laboratory projects—analyzing millions of manga pages, Instagram photos, artworks on DeviantArt, Van Gogh's entire painted output, and event data from hundreds of countries—Manovich introduces readers to the conceptual frameworks and practical challenges of treating culture as data. He explains how to move from elusive cultural phenomena to computable representations, how to sample cultural populations responsibly, how to extract meaningful features from images and other media, and how to visualize enormous collections in ways that reveal patterns invisible to any individual observer. Crucially, the book is also a sustained critical examination of what computational methods can and cannot see, resisting naive scientism while insisting that quantification opens genuinely new questions about creativity, style, diversity, and the long tail of global cultural production. Written accessibly for readers with no technical background, Cultural Analytics is both a hands-on introduction and a theoretically ambitious argument for a more inclusive, democratic, and computationally literate approach to the study of culture.

The through-line

Who it’s for
Researchers, students, and practitioners in the humanities, social sciences, media studies, arts, design, journalism, museums, and libraries who sense that the sheer scale of contemporary digital culture has outpaced their existing methods and want to engage seriously with it without abandoning their humanistic commitments.
The problem
Billions of people are creating and sharing digital media every day, producing a cultural universe so vast that no individual, institution, or existing humanistic method can observe, map, or understand it at scale. The researcher feels inadequate, peripheral, and vaguely fraudulent—aware that the canons and small samples they work with are unrepresentative, that entire continents of cultural production remain invisible to them, and that industry algorithms are shaping culture in ways they cannot study.
The plan
  1. Understand why the new scale of digital culture makes computational methods necessary rather than optional (Part I).
  2. Learn how to identify and collect the four types of cultural data: media artifacts, behavioral traces, interactions, and events/places/organizations (Chapter 4).
  3. Apply systematic sampling strategies—random, stratified, or complete-dataset approaches—rather than relying on canons or taste-based selection (Chapter 5).
  4. Convert cultural phenomena into structured datasets by selecting objects, choosing features (metadata and extracted characteristics), and encoding them in appropriate data types (Chapter 6).
  5. Understand the difference between linguistic categories and numerical features, and use the latter to represent analogical cultural dimensions with greater fidelity (Chapter 7).
The payoff
The researcher can see and map cultural production across thousands of cities and millions of creators rather than being confined to a handful of canonical centers. · Cultural arguments are grounded in representative samples and explicit sampling procedures rather than implicit canonical bias. · Numerical and visual representations allow the researcher to describe subtle aesthetic and stylistic variation that language alone cannot capture.

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Additional reading

  • La distinction (Distinction: A Social Critique of the Judgement of Taste) · Pierre Bourdieu

    Cited as a crucial predecessor for its pioneering use of quantitative methods (correspondence analysis) to map the relationship between cultural taste and social class, demonstrating an early form of 'cultural analytics'.

  • Graphs, Maps, Trees: Abstract Models for a Literary History · Franco Moretti

    Moretti's work on 'distant reading' in literary studies is presented as a parallel and influential approach that uses quantitative analysis and visualization to study large literary corpora, aligning with the goals of cultural analytics.

  • The Use of Computers in Anthropology · Dell H. Hymes (editor)

    The book is quoted to show that the core idea of using computers to 'see relations and patterns in masses of data' for cultural study was clearly articulated as early as 1965.

  • The Datafied Society: Studying Culture through Data · Karin van Es & Mirko T. Schäfer (editors)

    Recommended as a collection of articles that address the methodological and ethical issues related to using data and algorithms in academic research, providing critical context for cultural analytics.

  • Software Takes Command · Lev Manovich

    The author positions 'Cultural Analytics' as a logical successor to this earlier book, moving from a theoretical analysis of media creation software to the practical analysis of the culture produced with it.

  • Dialettica dell'illuminismo (Dialectic of Enlightenment) · Max Horkheimer and Theodor W. Adorno

    Their concept of the 'culture industry' is used as a historical baseline to contrast the 20th-century model of mass media with the 21st-century model based on computational 'media analytics'.

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