———HTNM REVISITED 11/03 VIDEO ONLINE———–
Miriam Posner’s History and Theory of New Media Lecture from September 29, 2016 is now live online on YouTube! Posner presented “A Workshop on Network Analysis for Film & Media” and introduced some of the basic components of network analysis, talked about why media scholars might find it useful, and connected the audience with some resources to build network analyses. We’re glad to have kicked off the 2016-2017 History and Theory of New Media lecture series with Miriam’s workshop!
———HTNM REVISITED 10/04 HTNM REVISITED———–
Miyoko Conley, a BCNM Designated Emphasis candidate in Theater, Dance, and Performance Studies, recaps Miriam Posner’s History and Theory of New Media Lecture Series workshop on network analysis for film & media, which took place on September 29, 2016.
The 2016-17 History and Theory of New Media lecture series kicked off on September 29th, with a combination lecture and workshop by Miriam Posner, entitled, “Network Analysis for Media Studies.” Posner, a Digital Humanities core faculty member at UCLA, introduced network analysis as a methodology, and showed how this valuable tool can open new channels for knowledge formation in the humanities.
The workshop began with the simple question, “What is network analysis?” Though a method and a tool used to track and analyze connections between data, Posner emphasized it as a way of understanding the world as a set of relationships. Network analysis can help scholars understand seemingly basic questions like, who knew whom, or how might ideas or formal motifs have traveled? However, network analysis also gives visual form to sets of relationships, which brings to light questions such as, have we [scholars] overlooked important figures in communities of practice? Does a certain community have unusual or unexpected properties? Throughout her demonstration, Posner referred back to not only this theme of relationships, but also to the challenge network analysis brings to dominant modes of thinking.
One way this challenge is issued is through the way network analysis programs lay out data. When looking at a set of nodes (entities) and edges (lines), the brain wants to assign significance to the proximity of particular nodes; however, network analysis graphs do not assign a particular value to proximity. Posner demonstrated this through her own analysis of filmmaker Oscar Micheaux’s cast network. While the example provided a step-by-step guide on how one might start data analysis, it also showed how one might begin to interpret the visual data, including avoiding the pitfall of assuming proximity indicates value.
After showing multiple ways to construct graphs and explaining terminology, Posner continued the theme of connectedness quite literally with a live demonstration. She gathered volunteers and connected them together with string, to illustrate the different ways connections can be read for importance. It is not always the same type of information that is given weight; for example, one might assume that the node with the most direct connections has the most importance, but that does not take into account the nodes that are connected to other clusters, and would therefore control the flow of information.
The question and answer session focused on the question of potential in network analysis. Many of the projects Posner cited illustrated how network analysis in the digital humanities can be used for exploration and experimentation, and to expose normalized structures of thought. This latter idea was also shown through another project in which Posner is involved, Maori Subject Headings, which strives to improve the accessibility of Maori materials in libraries. Since the Library of Congress’ subject headings do not match up with how Maori culture groups objects, Maori materials have become harder to search, particularly for Maori users. Through data analysis, Maori Subject Headings is one way to re-catalogue the information and decenter dominant knowledge structures. By looking at how data analysis offers a representational form for ways of knowing, Posner closed her workshop with not only excitement for its possibilities, but also with an emphasis on critically thinking about the data structures used.
Media companies and personnel often operate in networks. A company of actors may work together frequently, for example, or a certain set of directors might tend to move in the same circle. One fan community might have points of overlap with another, and certain companies might tend to be involved with each other in media production. When relationships like these are important, media scholars may find it useful to consider some of the techniques of network analysis, a field that specializes in untangling and analyzing the import of these connections. In this workshop, designed for novices, I’ll introduce some of the basic components of network analysis, talk about why media scholars might find it useful, and connect you with some resources so that you can build your own network analyses.
Miriam Posner is the Digital Humanities program coordinator and a member of the core DH faculty at the University of California, Los Angeles. As a digital humanist, she is particularly interested in the visualization of large bodies of data from cultural heritage institutions, and the application of digital methods to the analysis of images and video. A film, media, and visual culture scholar by training, she frequently writes on the history of science and technology. She is also a member of the executive council of the Association for Computers and the Humanities.
The History and Theory of New Media Lecture Series brings to campus leading humanities scholars working on issues of media transition and technological emergence. The series promotes new, interdisciplinary approaches to questions about the uses, meanings, causes, and effects of rapid or dramatic shifts in techno-infrastructure, information management, and forms of mediated expression. Presented by the Berkeley Center for New Media, these events are free and open to the public. For more information, visit: http://htnm-berkeley.com/