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PHILOSOPHICAL
LITERARY
JOURNAL
ISSN 0869-5377
Author: Manovich Lev

Manovich Lev

PhD in Visual and Cultural Studies, Director of the Software Studies Initiative at the University of California, San Diego (UCSD), Professor at The Graduate Center of the City University of New York. Address: 365 Fifth Avenue, New York, NY 10016, USA. E-mail: manovich.lev@gmail.com.

Publications

Media Visualization: Visual Techniques for Exploring Large Media Collections / Logos. 2015. № 2 (104). P. 66-91
annotation:  The digitization of historical visual collections and the emergence of user-generated visual content (Instagram, YouTube, and many other media sharing sites) create fascinating opportunities for cultural research, as well as a new problem. How can we work with sets of millions or billions of images and video? How can we discover interesting things in these collections, without prior knowledge of what we want to find? The article proposes visualization methods suitable for exploring massive collections of images and videos. It argues that existing mechanisms, such as search engines, are not effective for understanding patterns in a collection, since they only show items that match particular words or tags. It also critiques the exclusive use of verbal metadata, since it prevents us from seeing visual patterns not described by metadata. Instead, the author proposes to use the actual content of the collection—i. e., all images—and present them in various spatial layouts sorted in various ways. This allows us to see the patterns and the overall “shape” of a collection. To contrast this approach to the more familiar practice of data visualization, we have decided to call it “media visualization.” Typical information visualization involves translating the world into numbers and then visualizing relationships between these numbers. In contrast, media visualization involves translating a set of images into a new image that can reveal patterns in the set. In short, pictures are translated into pictures. Conceptually, media visualization is based on three operations: zooming out to see the whole collection (image montage), temporal and spatial sampling (using parts of a collection), and remapping (re-arranging the samples of media in new configurations).
Keywords:  media visualization; big data; media collections; software, patterns
How to Follow Software Users / Logos. 2015. № 2 (104). P. 189-218
annotation:  Software has replaced a diverse array of physical, mechanical, electronic technologies used before the 21st century to create, store, distribute and interact with cultural artifacts. It has become our interface to the world, to others, to our memory and our imagination—a universal language through which the world speaks, and a universal engine that the world runs on. What electricity and the internal combustion engine were to the early 20th century, software is to the early 21st century. This article looks at the cutting edge of modern media research. It discusses the problems of analysing interactive, software-driven processes of media consumption and creation—in other words, the “space” between users and programs. It also asks how the new paradigm of “digital humanities” (quantitative analysis of cultural datasets) can be used to study these interactive processes. Three questions are considered in particular: 1) What is the “data” in interactive media? Software code as it executes, the records of user interactions (for example, clicks and cursor movements), the video recording of a user’s screen, a user’s brain activity as captured by an EEG or FMRI, or something else? 2) Can analysis of software system code give us a detailed understanding of interactive cultural experiences? 3) Who has access to detailed records of user interactions with cultural artifacts and services on the web, and what are the implications of being able to analyze these data?
Keywords:  software studies; media theory; big data; software; interactivity; open source; critical code studies; cultural patterns
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