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I like to add the follwoing CMC-related sematic network analyses to Rice's list. Also, there are some research to apply the analysis into the S&T study at http://users.fmg.uva.nl/lleydesdorff/korea/
Paccagnella, L. ( 1998 ) ¡® Language, Network Centrality and Response to
Crisis in On-line Life: a Case Study on the Italian Cyberpunk Computer Conference ¡¯,
the Information Society 14 : 117 – 135
Smith, M. (1999c). Invisible crowds in cyberspace: Measuring and mapping the social structure of USENET. In Smith, M., & Kollock, P. (Eds.), Communities in cyberspace (pp. 195-219). London: Routledge.
Kang, N., & Choi, J. H. (1999). Structural implications of the crossposting network of international news in cyberspace. Communication Research, 26(4), 454-481.
Paolillo, J. C. (2001). Language variation on Internet Relay Chat: A social network approach. Journal of Sociolinguistics, 5(2), 180-213.
> -----Original message-----
> From: "Ronald E. Rice"
> To: [log in to unmask]
> Date: 2005/01/09(ÀÏ)10:29
> Subject: Re: Network Similarity and Language Use
> ***** To join INSNA, visit http://www.insna.org *****
> 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: "Jos?Garrois"
> Sent: Saturday, January 08, 2005 5:11 PM
> Subject: Network Similarity and Language Use
> > ***** To join INSNA, visit http://www.insna.org *****
> > 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,
> > Jos?De Fatima Garrois
> > ---------------------------------
> > _____________________________________________________________________
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Dr. Han Woo PARK
English site: http://www.hanpark.net
Department of Communication & Information
Yeung Nam University
214-1, Dae-dong, Gyeongsan-si,Gyeongsangbuk-do, South Korea, Zip Code 712-749
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