Cultural analytics

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Cultural analytics refers to the use of computational, visualization, and big data methods for the exploration of contemporary and historical cultures. While digital humanities research has focused on text data, cultural analytics has a particular focus on massive cultural data sets of visual material – both digitized visual artifacts and contemporary visual and interactive media. Taking on the challenge of how to best explore large collections of rich cultural content, cultural analytics researchers developed new methods and intuitive visual techniques that rely on high-resolution visualization and digital image processing. These methods are used to address both the existing research questions in humanities, to explore new questions, and to develop new theoretical concepts that fit the mega-scale of digital culture in the early 21st century.

Contents

History

The term "cultural analytics" was coined by Lev Manovich in 2007. After 2016, this term started to be increasingly used by other researchers, and many University programs in cultural analytics were gradually established. Journal of Cultural Analytics started to be published in 2016. Manovich's own monograph Cultural Analytics is being published by The MIT Press in the Fall of 2020.

Cultural analytics shares many ideas and approaches with visual analytics ("the science of analytical reasoning facilitated by visual interactive interfaces") and visual data analysis:

Visual data analysis blends highly advanced computational methods with sophisticated graphics engines to tap the extraordinary ability of humans to see patterns and structure in even the most complex visual presentations. Currently applied to massive, heterogeneous, and dynamic datasets, such as those generated in studies of astrophysical, fluidic, biological, and other complex processes, the techniques have become sophisticated enough to allow the interactive manipulation of variables in real time. Ultra high-resolution displays allow teams of researchers to zoom in to examine specific aspects of the renderings, or to navigate along interesting visual pathways, following their intuitions and even hunches to see where they may lead. New research is now beginning to apply these sorts of tools to the social sciences and humanities as well, and the techniques offer considerable promise in helping us understand complex social processes like learning, political and organizational change, and the diffusion of knowledge. [1]

While increased computing power and technical developments allowing for interactive visualization have made the exploration of large data sets using visual presentations possible, the intellectual drive to understand cultural and social processes and production pre-dates many of these computational advances. Charles Joseph Minard's famous dense graphic showing Napoleon's March on Moscow [2] (1869) offers a 19th-century example. Published in 1979, Pierre Bourdieu's historical survey of the cultural consumption practices of mid-century Parisians, documented in La Distinction, foregrounds the study of culture and aesthetics through the lens of large data sets. Most recently, Franco Moretti's Graphs, maps, trees: abstract models for a literary history [3] (published in 2005) along with many projects in the Digital Humanities reveal the benefit of large scale analysis of cultural material.

Current research

To date, cultural analytics techniques have been applied to user-generated content, films, animations, video games, comics, magazines, books, artworks, photographs, and a variety of other media content. The technologies used for analyzing and exploring large visual collections range from open-source programs that run on any personal computer to supercomputer processing and large-scale displays such as the HIPerSpace (42,000 x 8000 pixels). [4]

Methodologies

The methodologies used in cultural analytics includes the data mining of large sets of culturally-relevant data (such as studies of library catalogs, image collections, and social networking databases), statistics, exploratory data analysis, and machine learning. Image processing of still and moving video, with feature recognition as well as image data extraction is used to support research into cultural and historical change. Cultural analytical methodologies are deployed to study and interpret video games and other software forms, both at the phenomenological level (human–computer interface, feature extraction) or at the object level (the analysis of source code.)

Cultural analytics relies heavily on software-based tools, and the field is related to the nascent discipline of software studies. While the objects of a cultural analytical approach are often digitized representations of the work, rather than the work in its original material form, the objects of study need not be digital works in themselves.

Related methodologies include:

Related Research Articles

Information visualization Study of visual representations of data

Information visualization or information visualisation is the study of visual representations of abstract data to reinforce human cognition. The abstract data include both numerical and non-numerical data, such as text and geographic information. It is related to data visualization, infographics, and scientific visualization. One definition is that it's information visualization when the spatial representation is chosen, whereas it's scientific visualization when the spatial representation is given.

Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. One well known subject classification system for computer science is the ACM Computing Classification System devised by the Association for Computing Machinery.

Computational archaeology describes computer-based analytical methods for the study of long-term human behaviour and behavioural evolution. As with other sub-disciplines that have prefixed 'computational' to their name, the term is reserved for methods that could not realistically be performed without the aid of a computer.

Social network analysis Analysis of social structures using network and graph theory

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes and the ties, edges, or links that connect them. Examples of social structures commonly visualized through social network analysis include social media networks, memes spread, information circulation, friendship and acquaintance networks, business networks, knowledge networks, difficult working relationships, social networks, collaboration graphs, kinship, disease transmission, and sexual relationships. These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.

Scientific visualization interdisciplinary branch of science concerned with presenting scientific data visually

Scientific visualization is an interdisciplinary branch of science concerned with the visualization of scientific phenomena. It is also considered a subset of computer graphics, a branch of computer science. The purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data. Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.

Visualization (graphics)

Visualization or visualisation is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering and scientific purposes.

Lev Manovich

Lev Manovich is an author of books on new media theory, and professor of Computer Science at the Graduate Center, City University of New York. Manovich's research and teaching focuses on digital humanities, social computing, new media art and theory, and software studies.

Data visualization Creation and study of the visual representation of data

Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a time series.

Digital humanities

Digital humanities (DH) is an area of scholarly activity at the intersection of computing or digital technologies and the disciplines of the humanities. It includes the systematic use of digital resources in the humanities, as well as the analysis of their application. DH can be defined as new ways of doing scholarship that involve collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. It brings digital tools and methods to the study of the humanities with the recognition that the printed word is no longer the main medium for knowledge production and distribution.

Software studies is an emerging interdisciplinary research field, which studies software systems and their social and cultural effects. The implementation and use of software has been studied in recent fields such as cyberculture, Internet studies, new media studies, and digital culture, yet prior to software studies, software was rarely ever addressed as a distinct object of study. To study software as an artifact, software studies draws upon methods and theory from the digital humanities and from computational perspectives on software. Methodologically, software studies usually differs from the approaches of computer science and software engineering, which concern themselves primarily with software in information theory and in practical application; however, these fields all share an emphasis on computer literacy, particularly in the areas of programming and source code. This emphasis on analysing software sources and processes often distinguishes software studies from new media studies, which is usually restricted to discussions of interfaces and observable effects.

Visual analytics

Visual analytics is an outgrowth of the fields of information visualization and scientific visualization that focuses on analytical reasoning facilitated by interactive visual interfaces.

Cybermethodology is a newly emergent field that focuses on the creative development and use of computational and technological research methodologies for the analysis of next-generation data sources such as the Internet. The first formal academic program in Cybermethodology is being developed by the University of California, Los Angeles.

Netnography, an online research method originating in ethnography, is understanding social interaction in contemporary digital communications contexts. Netnography is a specific set of research practices related to data collection, analysis, research ethics, and representation, rooted in participant observation. In netnography, a significant amount of the data originates in and manifests through the digital traces of naturally occurring public conversations recorded by contemporary communications networks. Netnography uses these conversations as data. It is an interpretive research method that adapts the traditional, in-person participant observation techniques of anthropology to the study of interactions and experiences manifesting through digital communications.

The following outline is provided as an overview of and topical guide to formal science:

Digital sociology is a sub-discipline of sociology that focuses on understanding the use of digital media as part of everyday life, and how these various technologies contribute to patterns of human behavior, social relationships, and concepts of the self.

Parametric design

Parametric design is a process based on algorithmic thinking that enables the expression of parameters and rules that, together, define, encode and clarify the relationship between design intent and design response.

Visual computing is a generic term for all computer science disciplines dealing with images and 3D models, such as computer graphics, image processing, visualization, computer vision, virtual and augmented reality and video processing. Visual computing also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. The core challenges are the acquisition, processing, analysis and rendering of visual information. Application areas include industrial quality control, medical image processing and visualization, surveying, robotics, multimedia systems, virtual heritage, special effects in movies and television, and computer games.

Digital archaeology is the application of information technology and digital media to archaeology. It includes the use of digital photography, 3D reconstruction, virtual reality, and geographical information systems, among other techniques. Computational archaeology, which covers computer-based analytical methods, can be considered a subfield of digital archaeology, as can virtual archaeology.

Distant reading is an approach in literary studies that applies computational methods to literary data, usually derived from large digital libraries, for the purposes of literary history and theory. While the term is collective, and is used to refer to a range of different computational methods of analysing literary data, similar approaches also include macroanalysis, cultural analytics, computational formalism, computational literary studies, quantitative literary studies, and algorithmic literary criticism.

References

  1. ↑ "Four to Five Years: Visual Data Analysis". 2010 Horizon Report. The New Media Consortium. Archived from the original on 2011-08-10.
  2. ↑ "Edward Tufte: New ET Writings, Artworks & News". www.edwardtufte.com. Retrieved 2021-06-28.
  3. ↑ Moretti, Franco (2005). Graphs, maps, trees: abstract models for a literary history. Verso. p. 119. ISBN   1-84467-026-0.
  4. ↑ "HIPerSpace". Archived from the original on 2019-10-20. Retrieved 2011-07-16.
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