Cultural Analytics

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.

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.

The latest installment of a digital humanities bellwether Contending with recent developments like the shocking 2016 U.S. Presidential election, the radical transformation of the social web, and passionate debates about the future of data in higher education, Debates in the Digital Humanities 2019 brings together a broad array of important, thought-provoking perspectives on the field’s many sides. With a wide range of subjects including gender-based assumptions made by algorithms, the place of the digital humanities within art history, data-based methods for exhuming forgotten histories, video games, three-dimensional printing, and decolonial work, this book assembles a who’s who of the field in more than thirty impactful essays. Contributors: Rafael Alvarado, U of Virginia; Taylor Arnold, U of Richmond; James Baker, U of Sussex; Kathi Inman Berens, Portland State U; David M. Berry, U of Sussex; Claire Bishop, The Graduate Center, CUNY; James Coltrain, U of Nebraska–Lincoln; Crunk Feminist Collective; Johanna Drucker, U of California–Los Angeles; Jennifer Edmond, Trinity College; Marta Effinger-Crichlow, New York City College of Technology–CUNY; M. Beatrice Fazi, U of Sussex; Kevin L. Ferguson, Queens College–CUNY; Curtis Fletcher, U of Southern California; Neil Fraistat, U of Maryland; Radhika Gajjala, Bowling Green State U; Michael Gavin, U of South Carolina; Andrew Goldstone, Rutgers U; Andrew Gomez, U of Puget Sound; Elyse Graham, Stony Brook U; Brian Greenspan, Carleton U; John Hunter, Bucknell U; Steven J. Jackson, Cornell U; Collin Jennings, Miami U; Lauren Kersey, Saint Louis U; Kari Kraus, U of Maryland; Seth Long, U of Nebraska, Kearney; Laura Mandell, Texas A&M U; Rachel Mann, U of South Carolina; Jason Mittell, Middlebury College; Lincoln A. Mullen, George Mason U; Trevor Muñoz, U of Maryland; Safiya Umoja Noble, U of Southern California; Jack Norton, Normandale Community College; Bethany Nowviskie, U of Virginia; Élika Ortega, Northeastern U; Marisa Parham, Amherst College; Jussi Parikka, U of Southampton; Kyle Parry, U of California, Santa Cruz; Brad Pasanek, U of Virginia; Stephen Ramsay, U of Nebraska–Lincoln; Matt Ratto, U of Toronto; Katie Rawson, U of Pennsylvania; Ben Roberts, U of Sussex; David S. Roh, U of Utah; Mark Sample, Davidson College; Moacir P. de Sá Pereira, New York U; Tim Sherratt, U of Canberra; Bobby L. Smiley, Vanderbilt U; Lauren Tilton, U of Richmond; Ted Underwood, U of Illinois, Urbana-Champaign; Megan Ward, Oregon State U; Claire Warwick, Durham U; Alban Webb, U of Sussex; Adrian S. Wisnicki, U of Nebraska–Lincoln.

The canon of postwar American fiction has changed over the past few decades to include far more writers of color. It would appear that we are making progress—recovering marginalized voices and including those who were for far too long ignored. However, is this celebratory narrative borne out in the data? Richard Jean So draws on big data, literary history, and close readings to offer an unprecedented analysis of racial inequality in American publishing that reveals the persistence of an extreme bias toward white authors. In fact, a defining feature of the publishing industry is its vast whiteness, which has denied nonwhite authors, especially black writers, the coveted resources of publishing, reviews, prizes, and sales, with profound effects on the language, form, and content of the postwar novel. Rather than seeing the postwar period as the era of multiculturalism, So argues that we should understand it as the invention of a new form of racial inequality—one that continues to shape the arts and literature today. Interweaving data analysis of large-scale patterns with a consideration of Toni Morrison’s career as an editor at Random House and readings of individual works by Octavia Butler, Henry Dumas, Amy Tan, and others, So develops a form of criticism that brings together qualitative and quantitative approaches to the study of literature. A vital and provocative work for American literary studies, critical race studies, and the digital humanities, Redlining Culture shows the importance of data and computational methods for understanding and challenging racial inequality.

In a project to restructure Cultural analytics outcomes, which stakeholders would you involve? How can you measure Cultural analytics in a systematic way? What are the Essentials of Internal Cultural analytics Management? What is the total cost related to deploying Cultural analytics, including any consulting or professional services? Does Cultural analytics systematically track and analyze outcomes for accountability and quality improvement? This powerful Cultural analytics self-assessment will make you the established Cultural analytics domain assessor by revealing just what you need to know to be fluent and ready for any Cultural analytics challenge. How do I reduce the effort in the Cultural analytics work to be done to get problems solved? How can I ensure that plans of action include every Cultural analytics task and that every Cultural analytics outcome is in place? How will I save time investigating strategic and tactical options and ensuring Cultural analytics costs are low? How can I deliver tailored Cultural analytics advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Cultural analytics essentials are covered, from every angle: the Cultural analytics self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Cultural analytics outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Cultural analytics practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Cultural analytics are maximized with professional results. Your purchase includes access details to the Cultural analytics self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book.

This book covers computationally innovative methods and technologies including data collection and elicitation, data processing, data analysis, data visualizations, and data presentation. It explores how digital humanists have harnessed the hypersociality and social technologies, benefited from the open-source sharing not only of data but of code, and made technological capabilities a critical part of humanities work. Chapters are written by researchers from around the world, bringing perspectives from diverse fields and subject areas. The respective authors describe their work, their research, and their learning. Topics include semantic web for cultural heritage valorization, machine learning for parody detection by classification, psychological text analysis, crowdsourcing imagery coding in natural disasters, and creating inheritable digital codebooks.Designed for researchers and academics, this book is suitable for those interested in methodologies and analytics that can be applied in literature, history, philosophy, linguistics, and related disciplines. Professionals such as librarians, archivists, and historians will also find the content informative and instructive.

The ability to gather data that can be crunched by machines is valuable for studying society. The new methods needed to work it require new skills and new ways of thinking about best research practices. This book reflects on the role and usefulness of big data, challenging overly optimistic expectations about what it can reveal, introducing practices and methods for its analysis and visualization, and raising important political and ethical questions regarding its collection, handling, and presentation.

This book considers the challenges related to the effective implementation of artificial intelligence (AI) and machine learning (ML) technologies to the cultural heritage digitization process. Particular focus is placed on improvements to the data acquisition stage, as well as the data enrichment and curation stages, using advanced artificial intelligence techniques and tools. An emphasis is placed on recent applications related to deep learning for visual recognition, generative models, natural language processing, and super resolution. The book is a valuable reference for researchers working in the multidisciplinary field of cultural heritage and AI, as well as professional experts in the art and culture domains, such as museums, libraries, and historic sites and buildings. Reports on techniques and methods that leverage AI and machine learning and their impact on the digitization of cultural heritage; Addresses challenges of improving data acquisition, enrichment and management processes; Highlights contributions from international researchers from diverse fields and subject areas.