Networks of Stories, Structures and Digital Humanities

In March 2004, Slashdot, the online news
aggregator 'for nerds', cited a web page
featuring animated network visualizations
of the relationships between characters in
Shakespeare’s plays.
1
The Shakespeare site
began getting hits at a rate of 250,000 hits
per hour. Programmer Paul Mutton created the
diagrams by feeding Shakespeare’s plays into
an IRC (Internet Relay Chat) bot designed to
visualize social networks. The Slashdot story
promised that the diagrams would allow users to
“see Shakespeare in an entirely new light”.
2
Social network analysis, the mapping of
relationships as networks, has truly provided
new insights in the social and life sciences with
increasing participation from mathematicians
and computer scientists. The idea that there are
laws that govern networks--such as the notion
that everyone is at most six steps away from any
other person on earth, and that networks evolve
in predictable ways--has led to remarkable
discoveries in fields from sociology to biology.
These insights furthered publications such as
Six Degrees (by sociologist Duncan Watts)
3
presenting these discoveries (and the science
behind them) to a more general audience. In
addition, online communities such as Facebook,
Twitter, MySpace, etc., have demonstrated the
utility and power of network theory in our daily
lives.
With popular awareness of these tools and
techniques, combined with the promise of
new insights into ever-increasing amounts of
data, network analysis has significant appeal
to digital humanities scholars. However, these
investigations call for tools and software to
gather and clean large amounts of data,
even when the researcher opts for analyzing
only a tiny fraction of these vast data sets.
Moreover, to interpret these datasets, a good
understanding of network analysis, as well
as other visualization means is necessary.
Overcoming the hurdle of handling complicated
technological tools and acquisition of the
necessary expertise in network theory both
demand investment on the side of researchers,
a risky investment that only a handful of
humanities scholars are willing to make.
4
Typically, researchers familiar with the tools
perform these analyses, posing the questions
and interpreting the results. In this session,
we will first provide a brief overview of social
network analysis and related tools. Then, we
will focus on three examples of how we,
as humanities scholars, have made use of
network analysis tools and techniques in our
research, both to illustrate the potential of
these approaches, and to discuss some common
problems and their possible solutions.
The names and affiliation of confirmed authors
are as follows:
-
Zoe Borovsky, Academic Technology Services
and the Center for Digital Humanities,
University of California, Los Angeles
-
Wouter de Nooy, Amsterdam School
of Communication Research (ASCoR),
University of Amsterdam, The Netherlands
-
Loet Leydesdorff, Amsterdam School
of Communication Research (ASCoR),
University of Amsterdam, The Netherlands
-
Andrea Scharnhorst, Virtual Knowledge
Studio of the Netherlands Royal Academy of
Arts and Sciences, The Netherlands
-
Almila Akdag Salah, Virtual Knowledge
Studio of the Netherlands Royal Academy of
Arts and Sciences, The Netherlands
Notes
1.
http://www.jibble.org/shakespeare/

2
2.
http://tech.slashdot.org/story/04/03/11/01512
56/Tracking-Social-Networking-In-Shakespeare-
Plays?art_pos=24
Mutton also presented his work at a
conference on Information Visualization in 2004. See
http:/
/www.cs.kent.ac.uk/pubs/2004/1931/content.pdf
3.
Watts, D.J. (2003). Six Degrees: The Science of a Connected
Age 1st ed., W. W. Norton & Company
4.
Social network analysis is a more popular tool among social
scientist then humanities scholars. A search at Web of Science
for the search term returns around 150 hits for social sciences,
whereas if the search is refined to arts and humanities only, it
has less than 30 results.
Dickens' Double Narrative:
Network Analysis of Bleak
House
Zoe Borovsky
zoe@ats.ucla.edu
Academic Technology Services and the
Center for Digital Humanities, University of
California, Los Angeles
My presentation extends Masahiro Hori’s
collocational analysis of Dickens’ style (2004)
1
using network content analysis tools. Because
DH2010 will be held in London, and since
Hori devotes a chapter in his book to Dickens’
novel,
Bleak House
, I will focus the presentation
on this novel. My goals are three-fold: First,
I will show how network analysis provides
additional insights and metrics for studying the
collocational patterns in texts. Secondly, I will
demonstrate how this analysis provides new
insights into the gender issues in Dickens’ novel.
Finally, I will extend my analysis to the field of
Digital Humanities and reflect on how it, too,
can be read as a “double-narrative” with a deep
structure similar to the one Dickens depicts in
Bleak House
.
Network Analysis has been applied to
many different types of systems: sociological,
biological, ecological, as well as the Internet. In
addition, human language has been examined
for evidence of network behavior—the laws
that have been shown to govern other complex
systems. Beginning with the 2001 publication
of “The Small World of Human Language”.
2
I
will present a brief overview of scholarship on
network analysis to provide some background.
Typically, humanities scholars have used
network analysis to model networks described
in texts (e.g. the characters in Shakespeare’s
plays).
3
However, the main purpose of this paper
is to demonstrate the implications of these more
theoretical studies upon text analysis.
Having provided the context for my analysis,
I will show how Wordij,
4
a suite of network
content analysis tools designed to analyze
unstructured texts, can be used to extend Hori’s
analysis of the collocates
5
of the two narrators
in Dickens’ text. Against the prevailing view
that the first-person narrative of the central
female character, Esther, is plain and boring,
Hori compares the unusual collocates of both
narrators. His careful analysis reveals that
Esther’s narrative is “linguistically experimental
and satisfactorily creative” (Hori 2004, p.
206). My analysis builds upon Hori’s and
illustrates that through the course of the novel,
the narrative of the anonymous third-person
narrator becomes “tainted” with collocates that
have led scholars to judge Esther’s narrative as
overly sentimental and boring.
Because Wordij allows us to analyze texts as a
network, with links created between collocates,
we can use it to calculate the distance between
concepts that are connected through other links.
Using the network diagrams produced from
collocates of both these narrative sections, I
will demonstrate how this type of “semantic
map” can be used to compare the distance
between words. As examples, I compare the
distance between the words “man” and “woman”
in each narrator’s section. Opticomm (one of
the programs in Wordij) measures the distance
between nodes (the words) by counting the
number of links between words and calculating
the distance or path and the average pair
frequency.
For example, one of the shortest paths from
the word “man” to “woman” in Esther’s text
takes the following route: man
→
said
→
woman.
Opticomm calculates the “distance” using the
frequency of the pairs. The pair “man
→
said”
occurs 27 times (1/27 = .0370), and the pair
“said
→
woman” occurs 19 times (1/19 =.0526).
The sum of the distances equals .0897. The
average pair frequency of the path is low: 23. A
higher frequency path takes advantage of more
frequently used pairs. One of the “hubs” (a
word that has many connections) in Esther’s