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Here's some summariziation of semantic network analysis and related
approaches that have treated of the underlying issue you are getting at,
with various operationalizations in each:

            The essence of semantic network analysis is rather
straightforward (Danowski, 1988). Text is analyzed to determine some measure
of the extent to which words are related, which indicates something about
their meaning.  One measure of this relationship is the extent to which word
pairs co-occur within a given meaning unit.  Then, this measure of
relatedness across a set of words is used to group, cluster, or scale the
words (or some subset, such as the more frequently used words).  These
clusters can be directly interpreted, or used to derive more quantitative
measures for use in other analyses, or bases for formal content analysis.
Network approaches have been applied to the study of semantic memory and
association processes (Chang, 1986; Collins & Quillian, 1969; Flores-d'Arcais
& Schreuder, 1987), information retrieval algorithms and systems (Savoy,
1992), citation analysis (Callon, Courtial, Turner, & Bauin, 1983; Danowski
& Martin, 1979; Lievrouw, Rogers, Lowe, & Nadel, 1987; and Rice & Crawford,
1992), content analysis of traditional and CMC media (Cuilenburg,
Kleinnijenhuis, & de Ridder, 1986; Danowski, 1982), and responses to
open-ended survey questions (Carley & Palmquist, 1992; Rice & Danowski,
1993). Semantic network analysis using has been applied to understanding
positioning of candidates and issues in presidential debates (Doerfel &
Marsh, 2003), and the structure of interests in the International
Communication Association (Doerfel & Barnett, 1999), among other topics.
These and other prior studies provide the underlying arguments about
representing cognition and meaning through content associations.

Callon, M., Courtial, J-P., Turner, W., & Bauin, S. (1983).  From
translations to problematic networks: An introduction to co-word analysis.
Social Science Information, 2, 191-235.

Carley, K., & Palmquist, M. (1992).  Extracting, representing and analyzing
mental models.  Social Forces, 70, 601-636.

Chang, T.M. (1986).  Semantic memory:  Facts and models.  Psychological
Bulletin, 99, 199-220.

Collins, A.M., & Quillian, M.R. (1969).  Retrieval time from semantic
memory.  Journal of Verbal Learning and Verbal Behavior, 8, 240-247.

Consalvo, M., Baym, N., Hunsinger, J., Jensen, K.B., Logie, J., Murero, M. &
Shade, L.R. (Eds.). (2004). Internet research annual, vol. 1: Selected
papers from the Association of Internet Researchers conferences 2000-2002.
New York, NY: Peter Lang.

Cuilenburg, J.J. van, Kleinnijenhuis, J., & de Ridder, J.A. (1986).  A
theory of evaluative discourse: Towards a graph theory of journalistic
texts. European Journal of Communication, 1, 65-96.

Danowski, J. (1982).  A network-based content analysis methodology for
computer-mediated communication:  An illustration with a computer bulletin
board. In R. Bostrom (Ed.), Communication yearbook 6 (pp.  904-925).  New
Brunswick, NJ: Transaction Books.

Danowski, J. (1988). Organizational infographics and automated auditing:
Using computers to unobtrusively gather and analyze communication.  In G.
Goldhaber & G. Barnett (Eds.),  Handbook of organizational communication
(pp. 385-434).  Norwood, NJ:  Ablex.

Danowski, J.A. & Martin, T.H. (1979).  Evaluating the health of Information
Science:  Research community and user contexts. (Contract No. IST78-21130).
Washington, DC:  National Science Foundation.

Doerfel, M. L. & Barnett, G. A. (1999). A semantic network analysis of the
international communication association. Human Communication Research, 25
(4), 589-603.

Doerfel, M. L., & Marsh, P. S. (2003). Candidate-issue positioning in the
context of presidential debates. Journal of Applied Communication Research,
31, 212-237.

Flores-d'Arcais, G.B., & Schreuder, R. (1987).  Semantic activation during
object naming.  Psychological Research, 49, 153-159.

Jones, S. (2004).  Imaging an association.  In M. Consalvo, N. Baym, J.
Hunsinger, K.B. Jensen, J. Logie, M. Murero, & L.R. Shade (Eds.). (2004).
Internet research annual, vol. 1: Selected papers from the Association of
Internet Researchers conferences 2000-2002. (in press). New York, NY: Peter

Lievrouw, L., Rogers, E.M., Lowe, C.U., & Nadel, E. (1987). Triangulation as
a research strategy for identifying invisible colleges among biomedical
scientists.  Social Networks, 9, 217-248.

Rice, R.E., & Crawford, G. (1992).  Context and content of citations between
communication and library and information science articles.  In J. Schement
& B. Ruben (Eds.), Information and behavior 4 (pp. 189-217). New Brunswick,
NJ: Transaction Press.

Rice, R. E., & Danowski, J. (1993).  Is it really just like a fancy
answering machine? Comparing semantic networks of different types of voice
mail users.  Journal of Business Communication, 30(4), 369-397.

Savoy, J. (1992).  Bayesian inference networks and spreading activation in
hypertext systems.  Information Processing and Management, 28, 389-406.

Woelfel, J. (1991).  CatPac [Computer program]. Buffalo, NY: New York State
University, Department of Communication.


The following are some references I retrieved from the Ingenta, using author
names I'm familiar with, and terms such as "semantic network analysis" or
"semantic networks", along with some brief thoughts for why each might be

Monge and Contractor have developed a specific application/meaning for
semantic network analysis.  They argue that patterns of similarity in word
use, or interpretations of meanings (such as corporate mission statements)
should be associated with patterns of similarity in network/communication
relations.  I.e., you could have an affiliation matrix of people by
words/themes and then convert that into a people by people matrix, and then
test for an association of that with a people by people communication
network matrix. Thus, shared meaning could also be the basis for influencing
the development of network relations, such as in a knowledge management
system.   Some of these may discuss some of those concepts and results:

Managing Knowledge Networks. Authors: Contractor, N. S.; Monge, P. R.
Source: Management Communication Quarterly, November 2002, vol. 16, no. 2,
pp. 249-258.

Communication and Motivational Predictors of the Dynamics of Organizational
Innovation. Authors: Monge, Peter R.; Cozzens, Michael D.; Contractor,
Noshir S. Source: Organization science, 1992 , vol. 3, no. 2, pp. 250.

Network Theory and Small Groups. Authors: Katz, N.; Lazer, D.; Arrow, H.;
Contractor, N. Source: Small Group Research, June 2004, vol. 35, no. 3, pp.

Information Systems Division: Intrapersonal, Meaning, Attitude, and Social
Systems. Authors: Shapiro, M. A.; Lang, A.; Hamilton, M. A.; Contractor, N.
S. Source: Communication Yearbook, 2001 , vol. 24, pp. 17-50.

Formal and Emergent Predictors of Coworkers' Perceptual Congruence on an
Organization's Social Structure. Authors: Heald, Maureen R.; Contractor,
Noshir S.; Wasserman, Stanley.

Source: Human communication research, 1998 , vol. 24, no. 4, pp. 536.

Interactional Influence in the Structuring of Media Use in Groups: Influence
in Members' Perceptions of Group Decision Support System Use. Authors:
Contractor, Noshir S.; Seibold, David R.; Heller, Mark A. Source: Human
communication research, 1996 , vol. 22, no. 4, pp. 451.

Strategic Ambiguity in the Birth of a Loosely Coupled Organization: The Case
of a $50-Million Experiment. Authors: Contractor, Noshir S.; Ehrlich,
Matthew C. Source: Management communication quarterly, 1993 , vol. 6, no. 3,
pp. 251.

Structural Position and Perceived Similarity. Authors: Michaelson, Alaina;
Contractor, Noshir S. Source: Social psychology quarterly, 1992 , vol. 55,
no. 3, pp. 300.

The Use of Semantic Network Analysis to Manage Customer Complaints. Authors:
Fitzgerald, G. A.; Doerfel, M. L. Source: Communication Research Reports,
2004 , vol. 21, no. 3, pp. 231-242.

Semantic Connectivity: An Approach for Analyzing Symbols in Semantic
Networks. Authors: Carley, Kathleen M.; Kaufer, David S. Source:
Communication theory, 1993 , vol. 3, no. 3, pp. 183.

Intersubjective Semantic Meanings Emergent in a Work Group: A Neural Network
Content Analysis of Voice Mail. Authors: Sherblom, J. C.; Reinsch, N. L.;
Beswick, R. W. Source: Progress in Communication Sciences, 2001, no. 17, pp.

European Managers' Interpretations of Participation: A Semantic Network
Analysis. Author: Stohl, Cynthia. Source: Human communication research, 1993
, vol. 20, no. 1, pp. 97.

Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based
Social Networks. Authors: Golbeck, J.; Hendler, J. Source: Lecture Notes in
Computer Science, 2004, no. 3257, pp. 116-131.

Visualisation of Semantic Networks and Ontologies Using AutoCAD. Authors:
Mesina, M.; Roller, D.; Lampasona, C. Source: Lecture Notes in Computer
Science, 2004, no. 3190, pp. 21-29

Measuring Semantic Similarity Between Words Using Lexical Knowledge and
Neural Networks.

Authors: Li, Y.; Bandar, Z.; Mclean, D. Source: Lecture Notes in Computer
Science, 2002, no. 2412, pp. 111-116.

Semantic Networks in a Knowledge Management Portal. Author: Lebeth, K.
Source: Lecture Notes in Computer Science, 2001, no. 2174, pp. 463-466.

[these might be a good overview of the relation between cognitive maps and
semantic networks]

Cognitive Mapping Meets Semantic Networks. Author: Young, Michael D. Source:
The journal of conflict resolution, 1996 , vol. 40, no. 3, pp. 395.

A Conceptual Space Approach to Semantic Networks. Author: Hautamaki, A.
Source: Computers & mathematics with applications, 1992 , vol. 23, no. 6/9,
pp. 517.


Ronald E. Rice
Arthur N. Rupe Endowed Professor
Dept. of Communication, University of California
Incoming President of the International Communication Association
Co-Director, Center for Film, Television and New Media
Santa Barbara, CA 91306-4020
ph: 805-893-8696; fax: 805-893-7102
[log in to unmask]
----- Original Message -----
From: "JosŤ Garrois" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Saturday, January 08, 2005 5:11 PM
Subject: Network Similarity and Language Use

> *****  To join INSNA, visit  *****
> Dear All,
> I'm carrying out a social network study on language use in an electronic
> community. Data are drawn from the email archive containing all the
> messages exchanged among community members.
> The study hypothesizes a positive relationship between network similarity
> and  similarity in language use.
> I organized my data as following:
> -First, I divided the archive into 60 topic-specific email subsets (groups
> of emails on the same topic);
> -Second, for each of the 60 email subsets I built a two-mode matrix (ROWS
> = community members which sent or recieved at least one email on that
> topic; COLUMNS = email sent on that topic).
> -Third, I computed with UCINET VI a meaure of similarity among the columns
> of those 60 two-mode matrices. So, I got 60 square mail-x-mail matrices,
> where xij = value of network similarity between email i and email j. I
> call those matrices "Network-Similarity Matrices".
> -Fourth, I have other 60 square mail-x-mail matrices (one for each
> topic-specific subset), where xij = value of similarity between the TEXT
> of email i and the TEXT of email j. I call those matrices "Text-Similarity
> Matrices".
> Now, in order to test the relationship hypothesized above, I would like to
> do the following:
> -Building two diagonal matrices. The first one should have all the
> "network similarity matrices" on the diagonal and structural zeros
> elsewhere. The second one should be exactly the same with the "text
> similarity matrices" on the diagonal.
> -Run a QAP regression using those two big diagonal matrices as inputs.
> May I kindly ask you an opinion on these last two steps of my analysis? Do
> they make sense to you and what kind of weaknesses do you notice? Do you
> know other studies adopting a similar approach?
> Thanks a lot,
> Best Regards,
> JosŤ De Fatima Garrois
> ---------------------------------
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