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Affective Networks Annotated Bibliography

Page history last edited by Hannah Goodwin 8 years, 5 months ago

 

Annotated Bibliography

Team Affective Networks in Ensemble Character Dramas, by Alston D'Silva and Hannah Goodwin

 

[TEXT]

Easley, David, and John Kleinberg. Networks and Strategic Behavior: Reasoning about a Highly Connected World. Cambridge: Cambridge University Press, 2010. Print.

 

This book is co-authored by two Cornell professors, one of Computer Science and the other of Economics. It provides an excellent overview of how to analyze social networks using various frameworks, mostly mathematical. Its usefulness for a humanities scholar lies in the vocabulary it lays out, as well as some of the more basic concepts it discusses. Easley and Kleinberg usefully outline ways of looking at network structure in terms of ideas like balance, power, homophily, social capital, segregation, and equilibrium. In doing so they provide useful tools for looking at patterns of behavior, for modeling that behavior graphically, and for analyzing what network connections mean sociologically. The motivation of the book is quite interdisciplinary: the authors demonstrate the uses of social network theory for economics, epidemiology, sociology, engineering, psychology, and computer science, among other disciplines. Its more basic mathematical methodologies, accessible even to most humanities scholars, range from calculating the shortest distance between two nodes, to analyzing power within networks, to looking at network stability. While this is not the kind of book that lends itself to cover-to-cover reading, it is an immensely useful resource to turn to for help modeling and interpreting social networks.

 

 

Gandhi, Leela. Affective Communities: Anticolonial Thought and the Politics of Friendship. Delhi: Permanent Black, 2006.

 

Leela Gandhi’s Affective Communities is an examination of the politics of friendship as a vector that ruptures and bridges typical modes of associations, insisting that “affective gestures… refuse alignment along secure axes of filiation to seek expression outside, if not against, possessive communities of belong” (10). Gandhi is concerned here with charting the congruence of radical anti-imperial sentiment in Late Victorian England in individuals with their counterparts on the other side of the colonial encounter mounting similarly aligned cultural projects. However, Gandhi’s theorization might inform our own grappling with affective relationships across categorizations of normative relations, especially as it affords us new ways of understanding difference. 

 

Jenkins, Henry. “ ‘Infinite diversity in infinite combinations’: Genre and authorship in Star Trek. Science Fiction Audiences: Watching Doctor Who And Star Trek. Co-authors, Tulloch, John, and Henry JenkinsLondon: Routledge, 1995.

 

This chapter sits in a co-authored book on Science Fiction audiences, specifically the fan cultures following the Star Trek and Dr. Who franchises. Henry Jenkins and John Tulloch mostly write individually investigating specific fan formations and generally expanding on the project of Fan Cultures research. Chapter 9 is particularly interesting as it grapples with issues of authorship and genre that are implicated in the formalist register that methodologies in the digital humanities recalls. Jenkins describes the somewhat curious ascription of authorship of the Star Trek franchise by its fan to its creator Gene Roddenbery even as it acknowledges “the collaborative aspects of the production process” (188), let alone the fan productions he refers to.

 

Authorship, genre and other formal aspects of artwork return as crucial concerns in scholarly investigations of more recent media forms and objects. As digital humanities switch back and forth between inquiries within text to the labor of creators, whether they be the proprietary progenitors or their fanic progeny, Jenkins maneuverings here might prove useful in navigating our reconceptualizations. 

 

Moody, James. 2001. “Race, School Integration, and Friendship Segregation in America.” American Journal of Sociology 107 (3): 679–716. Web. 9 May 2012.

 

This article is the result of a sociological study of high school students and their social networks. It uses social network analysis to demonstrate the degree of racial interactivity in schools, which it displays in relationship to the level of heterogeneity in those schools. Moody discusses the concept of “homophily,” which he explains as a “well-known preference for similarity in social relations” (683), and uses graphs of social networks based on reported friendships to illustrate this trend. He argues that homophily the relationship between homophily and heterogeneity is non-linear; homophily is highest in schools with a middling amount of heterogeneity, and lowest both in highly heterogenous schools and in schools with the smallest minority populations. Moody looks for examples of integration and segregation in the networks he analyzes, defining integration as when friendships are formed independent of racial background, and segregation as when friendships do not form across racial divides.  He performs a mathematical analysis on the graphs, determining the degree of heterogeneity and segregation by using mathematical equations.

 

However, even without applying Moody’s more mathematical approach, a digital humanities scholar will find much of use in this article. Moody demonstrates through this article the usefulness to racial analysis of examining patterns that emerge through social network graphing.The graphs make visible trends in the formation of interracial friendships; for example, he notes that such friendships are more likely to occur in school where extracurricular activities are well-integrated. While he studies real-life situations, the methodology he uses to look at levels of interracial interaction can be applied to fictional works as well. Rather than looking at the expression of homophily in individual agents, a social network analysis approach to fictional works would examine homophilic biases, assumptions, observations, or statements of the works’ creators.

 

Williams, Raymond. Television: Technology And Cultural Form. New York: Schocken Books, 1975.

 

Television: Technology and Cultural Form is Raymond Williams’ classic treatment on television where he brings to bear his socio-historical and critical analysis to the medium. Here Williams critiques technological deterministic notions of television, attends to generic issues of television programming and introduces the influential notion of ‘flow.’

 

While Williams’s thinking may inform how we conceptualize television, works of deformance that reorder, interrupt or jar the sequences of a video text require we revisit his notion of ‘flow.’ Williams observes that the content of television should not be approached as discrete text but rather a segmented body interrupted and organized into a continuance sequence. He shifts from a treatment of this phenomenon across programming, observing the sequence of diverse types of shows, to examining television in the minute scale of scenes within a show and between commercials. Flow, therefore, excavates an organizing principle of television content—as Williams suggests, the disparate texts come together and “become[…] a mode of definition of an internal method” (93). Flow also implicates a spectator, describing that experience of fragmentary engagement and mean-making. Work of deformance that acts directly on the televisial text, formalistic as it may be, might benefit this framework of conceptualizing the simultaneously continuos and fragmentary quality of both the original text and its resultant deformance.

 

[TOOLS]

Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media. Web. 9 May 2012.

 

Gephi is an open source graph and data visualizing program coded in Java, and is downloadable for free from https://gephi.org/. It is accessible to the computer novice; no coding is involved and no special computer science skills are necessary. It is a tool for network analysis which allows for nodes to be grouped according to user-defined properties. Users can also program Gephi to run through simulations where data changes or visually reorganizes according to user-defined parameters. As well as being able to define and organize “node” data, Gephi appears to offer ways to define and elaborate “edges” as well, indicating for instance qualities such as directionality. Users may also upload .csv worksheets to import data sets.

 

Beginning user will likely find Gephi to be equally promising and frustrating. Some of key features are not intuitive and online tutorial support is quite limited. It is currently in its beta phase. As a data visualizing tool, it seems to exceed other programs such as Y-ed Graph Editor in certain respects, especially as it describes and makes legible complex networks. The method of entering data and turning that data into a graph is simple. It requires merely adding “nodes” and then dictating between which nodes edges should appear. Here is an example of the data entered textually:

 

 

 

However, once the graph appears, figuring out how to manipulate it productively has a steeper learning curve. And undoing the havoc one has inadvertently wrought on a formerly legible graph is somewhat frustratingly difficult.

 

 

With practice, though, one could use this tool quite productively. The graphs produced by experienced users are quite intricate and use color-coding and node size in ways that actually signify. It looks to be able to perform advanced functions for a practiced user. 

 

 

Gephi is a powerful visualization tool and can handle big data, but as data sets approach sizes in the hundreds or thousands, the program makes increasing demands on processing on the machine running it. 

 

ManyEyes. 2007. IBM Visual Communication Lab and the IBM Cognos software groupWeb. 

 

ManyEyes has the capacity to turn textual data into numerous kinds of graphic visualizations, including social network graphs. It is immensely user-friendly, and the time between entering the data and seeing a finished visualization is negligible, making this tool highly accessible to digital humanists. The process is automated, which has both benefits and drawbacks. It operates smoothly and involves little labor, but as a consequence, the user has very little role in shaping the outcome. The social network visualization tool does not allow for marking nodes either with color coding or size alteration, though it does size nodes automatically according to the number of edges they are connected to, with the most highly connected nodes being the largest. One other slight glitch is that only some of the nodes end up labeled, and this is somewhat random. Finally, edge length cannot be controlled, and seems to be dictated by the size of any particular cluster of a network. Thus the cluster with the most nodes will have longer edges, while "satellite" clusters have negligible space between nodes, making them less legible. That said, the graphs ManyEyes produces are aesthetically pleasing and capable of conveying information from datasets ranging from very small to very large. Here is a screenshot of a social network visualization in ManyEyes:


 

[WIKIS] 

“Friday Night Lights.” Fan Forum. 2012. Web. 9 May 2012. (http://www.fanforum.com/f243/index4.html)

 

This website houses an extensive fan forum for the fans of Friday Night Lights. Its usefulness for social network graphing lies largely in the structure of the threads, many of which are devoted to specific relationships between characters. For example, there are several threads that focus on one of the primary relationships of the film, that between the football coach and his wife. These threads are given titles such as “Mr. and Mrs. Coach [E&T] #13 ~No matter where they are, They are still The Heart of Texas~,” or “Mr. and Mrs. Coach [E&T] #14: Because Coach would love to start Tami,” and include in their content still photographs of the couple taken from multiple episodes across the show’s five seasons, quotes from their shared scenes, and even quotes from devoted fans describing their attachment to the fictional relationship. The ensuing discussion gives fans room to discuss the characters and the ins and outs of their relationship, including predictions and hopes for its future. Such threads exist not only for romantic relationships, but also for particularly affecting friendships or close familial bonds. The information contained in the thread titles alone are ripe for social network analysis, and the kind of commentary such threads engender gives an excellent sense of the affective nature of the relationships. 

 

 

The Internet Movie Database (IMDb), http://www.imdb.com/

 

The Internet Movie Database (IMDb), currently a subsidiary of Amazon, is something like an almanac of cinema and television. Its offering on television shows are surprisingly extensive and comprehensive. It operates as a heavily edited wiki, where users who in addition to contributing to ratings and discussion forums are also allowed to submit information on cast and crew credits that are apparently verified by staff. IMDb While not exhaustive (but seemingly close to it), most television shows have entries (i.e. dedicated pages) on most individual episodes. While IMDb once had extensive plot summaries, individual episodes are now available for rent via Amazon’s Prime service and consequently, these entries are more truncated. However, their information on each episode in terms of talent involved are incredibly detailed, listing even uncredited cast and crew. In addition, not only does IMDb have individual pages for actors, but curiously for the somewhat commercial platform, also has individual pages for the characters in the television shows.

 

IMDb offers dense, comprehensive, well-organized data on television shows. Most tantalizingly, IMDb also provides its database in pure text format for personal, non-commercial use (http://www.imdb.com/interfaces#plain). There have been some promising resources developed to exploit this feature. 

 

 

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