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.

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.

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.

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.

Deep Search collects 13 texts which investigate the social and political dimensions of how we navigate the deep seas of knowledge. What do we win, and what do we lose when we move from an analogue to a digital information order? How is computer readable significance produced, how is meaning involved in machine communication? Where is the potential of having access to such vast amounts of information? What are the dangers of our reliance on search engines and are there any approaches that do not follow the currently dominating paradigm of Google? This volume answers these questions of culture, context and classification regarding information systems that should not be ignored.

As more and more aspects of everyday life are turned into machine-readable data, researchers are provided with rich resources for researching society. The novel methods and innovative tools to work with this data not only require new knowledge and skills, but also raise issues concerning the practices of investigation and publication. This book critically reflects on the role of data in academia and society and challenges overly optimistic expectations considering data practices as means for understanding social reality. It introduces its readers to the practices and methods for data analysis and visualization and raises questions not only about the politics of data tools, but also about the ethics in collecting, sifting through data, and presenting data research.