16. May 2012
Subject: Re: Citation for exponential growth of papers about social networks? From: "Robert Ackland" <robert.ackland@ANU.EDU.AU> Date: Wed, September 16, 2009 14:33 To: SOCNET@LISTS.UFL.EDU
Loet Leydesdorff wrote:
» If we are defining a social network as is done in the field of social
network analysis (SNA), then as far as I'm aware, citation networks
are typically not modelled as social networks.
It is, in my opinion, not only a question of different models, but also of
different theorizing. Citation networks do not necessarily reflect social
networks among the authors citing each other because one may cite without
knowing the author. The communication can be mediated and organized by
intellectual content.
Communication network analysis may therefore be a discipline in itself. The
communications can circulate with a dynamics different from the relations
among communicators; for example, mediated by different communication media.
I agree that it is to do with different theorizing - and particular theories lead to particular empirical approaches. There is also the “home discipline” factor that Joshua mentions, and this can lead to people talking at cross-purposes because of the different conceptualisations of what a social network is (but I'm sure this has been raised before on this list).
Just to clarify: my response to Joshua was that (as far as I'm aware) citation networks are _typically_ not analysed as social networks (using the SNA definition of a social network), not that they are _never_ analysed as social networks.
There is no doubt that a citation network (node=paper, edge=citation from paper x to paper y) is a socially-constructed network, but is it a social network exhibiting structural signatures associated with social norms of behaviour e.g. reciprocity, transitivity?
As Joshua pointed out, there are mechanical reasons preventing such features in citation networks: the paper needs to be published before you can cite it, so reciprocity can't really happen. But if we focus on networks of authors (constructed from citation patterns) rather than networks of papers, then you can potentially get reciprocity, transitivity etc. i.e. it could be modelled as a social network. However, based on my initial investigations (and Ronald's list of references will be very helpful here) most empirical work in this area either implicitly or explicitly assumes that such networks reflect normatively-endorsed behaviour in science: the payment of intellectual debt, rather social norms (leading to transitivity, reciprocity and other formations found in social networks). And the empirical techniques that are used largely reflect this assumption e.g. counts regressions (focusing on attributes of citer/citee) rather than techniques that can explicitly model endogenous/self-organising network behaviour.
I mentioned ERGM simply because it is increasingly used and I had been puzzled why I hadn't seen it in bibliometric research, and figured it was due to theory driving particular empirical approaches. But someone offlist has also noted that the technique is still gaining attention and so not everyone knows about it. Regarding Loet's comment that ERGM is equivalent to cellular automata - I'll have to leave that to other list members more expert in ERGM than I.
Finally, I would add the to Ronald's list the following paper, which I found very useful:
Baldi, S. (1998). Normative versus social constructivist processes in the allocation of citations: A network-analytic model. American Sociological Review, 63, 829-846.
Rob
That is, they are not considered to exhibit the purely-structural or
endogenous network effects that are generally found in social
networks, e.g. transitivity, reciprocity. Otherwise, there
would have
been a recent (exponential?) surge in the use of exponential random
graph models (ERGM) to analyse citation networks, something that I
have not seen (although would be interested to hear of examples).
You find some studies using cellular automata in journals like JASSS.
Another relevant field would be around Systems Research & Behavioral
Science.
Network scientists (applied physics) and bibliometricians
(information
science) studying citation networks use techniques other than
ERGM. I
would argue that this is because they are conceptualising a citation
network as being something different to a social network
(again, using
the SNA definition of social network).
The semantics of SNA is very special because of the graph-theoretical
background. Nodes are redefined as vertices and links as arcs or edges.
Coming from another tradition I sometimes have to make a translation in my
mind. For example, “ERGM” is something equivalent to cellular automata,
isn't it?
Best wishes,
Loet
Rob
–
————————————-
Dr Robert Ackland
Fellow and Masters Coordinator,
Australian Demographic and Social Research Institute,
The Australian National University
e-mail: robert.ackland@anu.edu.au
homepage: http://adsri.anu.edu.au/people/robert.php
project: http://voson.anu.edu.au
Information about the Master of Social Research
(Social Science of the Internet specialisation):
http://adsri.anu.edu.au/study/msr.php
————————————-
Joshua O'Madadhain wrote:* To join INSNA, visit http://www.insna.org *
Jennifer:
What definition of “social network” are you using that excludes
“citation network”?
Joshua
On Sun, Sep 13, 2009 at 11:50 AM, Jennifer Kurkoski
kurkoski@haas.berkeley.edu wrote:* To join INSNA, visit http://www.insna.org * Hi folks,
I know I've seen it – that pretty exponential curveshowing exponential
growth in the publication of papers with social networksas the subject. But
my searches keep turning up papers about citation networks.
Can anyone point me in the right direction?
Many thanks!
- Jennifer
–
Jennifer Kurkoski
Ph.D. Candidate in Business Administration
Organizational Behavior
University of California, Berkeley
Subject: Re: Citation for exponential growth of papers about social networks? From: "Philip Topham" <ptopham@LNXRESEARCH.COM> Date: Wed, September 16, 2009 16:23 To: SOCNET@LISTS.UFL.EDU
My two hay-pennies worth. (half pennies) :)
In building “social networks” from citations the reciprocity issue always bothered me. And thus I've not pursued much.
In most cases I consider the back and forth citation indicative of a cooperative or at least ideologically respectful social communication; but in some cases were ones prestige is measured by the prestige of your rivals, an antagonistic relationship, in citing each other's may occur. So if you create a co-citation network of only reciprocal citations would the network be distorted by few “antagonistic” co citations when normally most co citations are “cooperative” in nature of most research. And even in that last statement I really don't know the amount of “cooperative” (positive links) versus “antagonistic” (negative links) actually exist in various research disciplines.
Has anyone looked at this aspect? Used any sort of text mining to assess sentiment?
All the best
Philip Topham Lnx Research
Subject: Re: Citation for exponential growth of papers about social networks? From: "Iain Lang" <iain.lang@PMS.AC.UK> Date: Wed, September 16, 2009 16:33 To: SOCNET@LISTS.UFL.EDU
These “antagonistic” citations are often called “negative citations”. Rob Ackland has already recommended a paper by Stephane Baldi but there's an earlier paper by Baldi and Lowell Hargens that deals with negative citations and which you might find of interest: Baldi S, Hargens LL. 1004. Reassessing the N-rays reference network: The role of self citations and negative citation. Scientometrics 34(2):239-253.
Eugene Garfield also had something to say about negative citations, and posed the question of whether they should be considered “bad” - given that science is about disproving things as much (or more) than it is about proving things, we might expect to see more negative citations than we do. See, e.g.: http://www.garfield.library.upenn.edu/ci/chapter10.pdf
Iain
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Dr Iain Lang Public Health Directorate, Devon PCT / Epidemiology & Public Health Group, Peninsula Medical School tel. +44 (0)1392 406749 email. iain.lang@pms.ac.uk
Subject: AAAS symposium Interdisciplinary Approaches to the Study of Large-Scale Human Networks From: "david lazer" <david_lazer@HARVARD.EDU> Date: Tue, February 10, 2009 03:19 To: SOCNET@LISTS.UFL.EDU
folks in chicago this friday might find this symposium at AAAS of interest. dl
* Interdisciplinary Approaches to the Study of Large-Scale Human Networks*
Date/time: Friday, Feb 13, 2009, 1:30 PM - 4:30 PM Location: HRC Columbus GH
Symposium abstract: There is a small but rapidly emerging thread of research on large-scale human networks. This work is based on the digital traces people leave behind of their communications, through their use of e-mail, mobile phones, instant messaging, and many other tools of the information age. Until recently, the study of social networks (largely within sociology) was almost exclusively based on surveys of who has relationships with whom. As a result, most research on networks has involved a single snapshot of a small human system. In contrast, the more recent work on call log and instant messaging data involves massively longitudinal data on millions or hundreds of millions of people. It is, however, unclear what these data can tell us. What is the significance, for example, of a phone call between two people? Does it signify, for example, a friendship or a wrong number? The objective of this symposium is to pull together an interdisciplinary panel to discuss the scientific potential of these emerging large-scale network data. Disciplines represented include physics, information science, communications, sociology, medicine, political science, and computer science.
David Lazer, Harvard *Life in the Network: The Coming Age of Computational Social Science* Large-scale network analysis is based on massive amounts of observations of communication behavior, while small scale network analysis has been based on self report data. Whereas the scientific relevance of the latter has been well established, based on decades of research, it is less clear what the scientific significance of the former. This talk, using illustrations from a variety of data sources, will examine the scientific potential of large scale network analysis.
Albert-Laszlo Barabasi, Northeastern University, Boston, MA *People in Motion: Studying Human Movement Based on Mobile Phone Data* The next challenge of network research is to go beyond the structure and quantify the dynamics of interconnected systems. A particular difficult facet of this research requires us to understand the temporal and spatial driving forces that govern social, technological and biological networks. I plan to focus on the opportunities offered by large datasets collected by mobile phone carriers to explore the dynamical mechanism that drive the activity of social networks as well as the travel pattern of individuals in social systems.
Alex Pentland, MIT *Honest Signals Predict Outcomes in Face-to-Face Interaction Networks* We have developed a wearable sensor `badge', called a sociometer, and used it to analyze thousands of hours of face-to-face interactions among networks of hundreds of people in common situations. These experiments demonstrate that up to 40% of variation in human behavior can be attributed to biological `honest signaling,' an unconscious, evolutionarily ancient communication channel. We demonstrate that these honest signals play a fundamental role in human decision making, and are predictive of outcomes in social situations ranging from dating to sales to business management and productivity.
Noshir Contractor, Northwestern *Digital Traces: An Exploratorium for Understanding and Enabling Social Networks* Recent advances in digital technologies invite consideration of organizing as a process that is accomplished by global, flexible, adaptive, and ad hoc networks that can be created, maintained, dissolved, and reconstituted with remarkable alacrity. These technologies also provide comprehensive digital traces of social actions, interactions, and transactions. These data provide an unprecedented Exploratorium to model the socio-technical motivations for creating, maintaining, dissolving, and reconstituting knowledge and social networks. Using examples from research in a wide range of activities such as disaster response, digital media and learning, public health and massively multiplayer online games (WoW - the World of Warcraft), Contractor will present a visual-analytic framework that is being used to Discover, Diagnose, and Design our social and knowledge networks.
Alessandro Vespignani, Indiana University *Mobility Networks and Contagion Processes* Transportation and mobility networks vary over many time and spatial scales and span international, inter-cultural and linguistic boundaries. The multi-scale nature and complexity of these networks are crucial features in the understanding of epidemic, contagion and connectivity processes in both the biological world and the ITC domain defined by the novel WiFi technologies. The presentation will discuss the central statistical features of these networks and the recently developed mathematical tools for the study of weighted and time dependent complex networks. Finally, we will review the impact of the complex features of mobility networks in the definition and study of stylized and realistic contagion models.
====== David Lazer (www.davidlazer.com) Director, Program on Networked Governance Associate Professor of Public Policy Harvard Kennedy School Harvard University The netgov blog: http://www.iq.harvard.edu/blog/netgov/
Search on Google for “Jones Nagin Trajectory”
http://ann.sagepub.com/cgi/reprint/602/1/82
Subject: us level county political data From: "Patrick Doreian" <pitpat+@pitt.edu> Date: Thu, February 5, 2009 03:52 To: "anuska ferligoj" <anuska.ferligoj@fdv.uni-lj.si> (less) "vladimir batagelj" <vladimir.batagelj@fmf.uni-lj.si> Cc: "natasa kejzar" <natasa.kejzar@fdv.uni-lj.si>
dear nusa and vlado (and natasa),
two of my tasks were to do some reading for the 'spatial clustering chapter and to locate data sources for temporal county level data, regarding the first, i have started reading The Big Sort and that led me to the following website:
http://www.uselectionatlas.org/
it has a lot of data - and i mean a lot of data. but it will be somewhat expensive. you might want to check out the site. at a first glance, it seem worthwhile to get some data from this source. but we will have to decide how much data and how much we are willing to pay. i have a small amount of research funds that could get some data - but not all of it. on the political side, i think that presidential voting makes the most sense if we want to cover the whole country because they are the only data that are national in scope and also uniform.
with best wishes
pat
Subject: SNA and spatial analysis - summary of replies
From: "Olga Mayorova" <ovm@email.arizona.edu> Date: Mon, July 14, 2008 22:20 To: SOCNET@LISTS.UFL.EDU Dear SocNetters, Here is a summary of replies to my earlier request of references on SNA and spatial analysis. Best, Olga Mayorova
Subject: geo-spatial enabled network analysis From: "Kathleen Carley" <kathleen.carley@CS.CMU.EDU> Date: Mon, July 14, 2008 22:49
ORA (see http://www.casos.cs.cmu.edu/projects/ora/ ) has tools for displaying networks on maps, and combining spatial and social networks. There are links to both google earth and arc-gis.
From: “Mark Newman” mejn@UMICH.EDU Date: Mon, August 4, 2008 18:37
This article seems to be getting a lot of press, but do read also the accompanying news piece by Couzin in the same issue of Science (highlighted by Barry Wellman in his email earlier today). Couzin references some other studies that have found that scientists are actually reading more, older, and more diverse papers than previously.
Mark
Filippo Menczer and friends have done some research suggesting that search on Google may actually create more heterogeneity. Perhaps the homogenizing effect observed by Evans doesn't have anything to do with the our search media. It could be a number of other things.
The articles are:
The egalitarian effect of *search *engines<http://arxiv.org/abs/cs.CY/0511005> AF Santo Fortunato, F *Menczer*, A Vespignani - Arxiv preprint cs.CY/0511005, 2005 - arxiv.org
Googlearchy or Googlocracy<http://www.cs.siue.edu/wwhite/IS376_Spring2006/Assignments/IS376Paper3.pdf> F *Menczer*, S Fortunato, A Flammini, A Vespignani - IEEE Spectrum, 2006 - cs.siue.edu
Thomas
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Thomas Hills
I am seeing a pattern… the more we lower barriers/friction to search/discovery/grouping the more individuals tend toward homophily. Does the internet speed up birds of a feather flocking together?
I know Cass Sunstein has written about this in Republic.com (2.0) and I keep seeing various on-line clusters with strong homophily and with political book-buying on-line – where it is very easy to view what others have said about a book, and what other authors are aligned with a particular author:
http://www.orgnet.com/divided.html
It is ironic, because the internet is always framed as allowing easy access to difference, diversity, & variety.
Valdis
On Aug 4, 2008, at 9:42 AM, Rick Davies wrote:
* To join INSNA, visit http://www.insna.org *
Hi all
There is an interesting possible connection here to evolutionary search
strategies, which I have only a brief aquantance with. Searching very
locally tends to produce sub-optimal solutions, because the search gets
stuck on local optima. Introduction of a random element into search
strategies, helps get past these to other solutions that are more distant. I
think genetic algorithms have both elements: a recombination of existing
approaches and a random (mutation) element.
What would therefore be useful is a randomised listing facility in online
journals. My 'umble website at www.mande.co.uk includes such a list of
postings from amongst all of those on the website (on right sidebar), as
well as listing of most recent posts. I deliberately decided not to list the
most popular posts, because I thought this would have the same sort of
negative effect as described below.
regards, rick davies
On Mon, Aug 4, 2008 at 4:23 PM, Valdis Krebs valdis@orgnet.com
wrote:
* To join INSNA, visit http://www.insna.org *
Interesting article on the affect of putting scholarly journals on-
line…
Electronic Publication and the Narrowing of Science and ScholarshipOnline journals promise to serve more information to more dispersed
audiences and are more efficiently searched and recalled. But because they
are used differently than print—scientists and scholars tend to search
electronically and follow hyperlinks rather than browse or
peruse—electronically available journals may portend an ironic change for
science. Using a database of 34 million articles, their citations (1945 to
2005), and online availability (1998 to 2005), I show that as more journal
issues came online, the articles referenced tended to be more recent, fewer
journals and articles were cited, and more of those citations were to fewer
journals and articles. The forced browsing of print archives may have
stretched scientists and scholars to anchor findings deeply into past and
present scholarship. Searching online is more efficient and following
hyperlinks quickly puts researchers in touch with prevailing opinion, but
this may accelerate consensus and narrow the range of findings and
ideas built upon.
Subject: Re: studies that have combined content and/or discourse analysis with SNA From: "Jana Diesner" <jdiesner@ANDREW.CMU.EDU> Date: Tue, November 2, 2010 13:25 To: SOCNET@LISTS.UFL.EDU
Emma
There are different approaches for how to represent co-occurrence of terms or themes in texts as structured data.
Some semantic network approaches use proximity to identify links, others use syntax or semantics � and the cutting edge ones typically combine these features. Below a little bit of everything. If you look for something more specific feel free to send me an email. We wrote some review book chapters on this topic, but only the one in German is out yet (also below).
Tool-wise, you might find AutoMap useful (http://www.casos.cs.cmu.edu/projects/automap/).
dynamic network analysis toolkit. Decision Support Systems (DSS), 43(4), 1324-1347.
complex discursive systems: Centering resonance analysis of communication. Human Communication Research, 28(2), 157-206.
Communication Sciences, 12, 198-221.
topic modeling and its application to innovation diffusion. IEEE International Conference on Social Computing (SocComp), Workshop on Finding Synergies Between Texts and Networks, Minneapolis, MN, August 2010. (URL: http://www.andrew.cmu.edu/user/jdiesner/publications/ieee_socComp_2010_diesn er_carley.pdf)
Meta-Matrix Text Analysis as a novel method for Network Text Analysis. In V. K. Narayanan & D. J. Armstrong (Eds.), Causal Mapping for Information Systems and Technology Research (pp. 81-108). Harrisburg, PA: Idea Group Publishing.
German, title: Extraktion relationaler Daten aus Texten). In C. Stegbauer & R. Häußling (Eds.), Handbook Network Research (Handbuch Netzwerkforschung). Vs Verlag. ← If you read German, this is a review on this topic
of research and methodologies. Connections, 21(2), 16-26.
B. (2009). Web Science 2.0: Identifying Trends through Semantic Social Network Analysis. IEEE Conference on Social Computing.
network analysis with semantic relations to support the evolution of a scientific community. 8th iternational conference on Computer supported collaborative learning.
networks. Social Networks.
extracting, representing, and querying media content. Charleston, SC: BookSurge Publishers.
Bobrow & A. Collins (Eds.), Representation and understanding: Studies in cognitive science (pp. 35-82). New York, NY: Academic Press.
Jana Diesner
PhD Cand.
Carnegie Mellon University
School of Computer Science
Center for Computational Analysis of Social and Organizational Systems
Web: http://www.andrew.cmu.edu/user/jdiesner/
Subject: Re: studies that have combined content and/or discourse analysis with SNA -- long list of references.... From: "Ronald E. Rice" <rrice@COMM.UCSB.EDU> Date: Tue, November 2, 2010 19:28 To: SOCNET@LISTS.UFL.EDU
Some resources collected from various prior SOCNET and AoIR posts and my own research. Ron Rice Nov 2010:
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.
SomeLinks
Corman, S. quantitative text and discourse analysis on the ASU website: http://www.public.asu.edu/~corman/
King, G. and Hopkin, D.'s program called ReadMe (a software package for R): http://gking.harvard.edu/readme/
Pajek: http://www.leydesdorff.net/indicators/index.htm
Pajek: http://www.leydesdorff.net/software/fulltext.exe
Pajek: http://www.leydesdorff.net/software/ti.exe,
i.Exe (at http://www.leydesdorff.net/software/ti ) uses as input a set of lines (e.g., titles; < 1000 characters) and generates the word/document occurrence matrix in the Pajek format. Additionally, a file “cosine.dat” is produced which normalizes the word occurrences using the cosine. Alternatively, one can import the output in Pajek or UCINet and generate the 1-mode affiliations matrices.
Internet Community Text Analyzer (ICTA) - http://textanalytics.net
A presentation, podcast, sample dataset, and a paper explaining the process of examining interview text data through a process involving Atlas.ti, SPSS and Multinet, available at: http://homepages.utoledo.edu/wmcketh/
Ackland, R., Gibson, R., Lusoli, W. and S. Ward (2010), “Engaging with the public? Assessing the online presence and communication practices of the nanotechnology industry,” forthcoming in Social Science Computer Review, accepted 19 June 2009. Pre-print: http://voson.anu.edu.au/papers/Nanotech_SSCORE_2010.pdf
Aral, S. and Van Alstyne, M. W. (2008). “Networks, Information & Social Capital (formerly titled 'Network Structure & Information Advantage')” (January 26). Available at SSRN: http://ssrn.com/abstract=958158
Aral, S. and Van Alstyne. M. W. (2009) “Networks Information and Brokerage: The Diversity-Bandwidth Tradeoff” Available at SSRN: http://ssrn.com/abstract=958158
Bearman, Peter S and Katherine Stovel. Becoming a Nazi: Models for Narrative Networks. 2000. Poetics. 27:69-90.
Blaschke, S., Schoeneborn, D., and Seidl, D. (2009). Organizations as Networks of Communications: A Methodological Proposal. Working Paper No. 102, University of Zurich. http://ssrn.com/abstract=1512471
Burt, R. (2008) “Information and Structural Holes: Comment on Reagans and Zuckerman.” Industrial and Corporate Change, 17(5), pp. 953-69. Available at: http://icc.oxfordjournals.org/cgi/content/full/dtn033v1
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 M. and Kaufer, D. 1993, “Semantic Connectivity: An Approach for Analyzing Semantic Networks,” Communication Theory, 3(3): 183-213.
Carley, K M. and Michael Palmquist, 1992, “Extracting, Representing and Analyzing Mental Models,” Social Forces, 70(3): 601-636
Carley, K M., 1988, “Formalizing the Social Expert's Knowledge,” Sociological Methods and Research, 17(2): 165-232.
Carley, K M., 1993, “Coding Choices for Textual Analysis: A Comparison of Content Analysis and Map Analysis, in Sociological Methodology: 23, p. 75 - 126. Reprinted in Content Analysis, edited by Roberto Franzosi, December, 2007, SAGE Publications, London
Carley, K M., 1993, “Content Analysis,” in Asher R.E. et al. (Eds.), The Encyclopedia of Language and Linguistics. Edinburgh, UK: Pergamon Press. Vol. 2: 725-730.
Carley, K M., 1994, “Extracting Culture through Textual Analysis,” Poetics, 22: 291-312.
Carley, K M., 1997, “Extracting Team Mental Models through Textual Analysis,” Journal of Organizational Behavior, 18: 533-538.
Carley, K M., 1997, “Network Text Analysis: The Network Position of Concepts,” Chapter 4 in C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 79-100.
Carley, K M., Jana Diesner, Jeffrey Reminga, and Maksim Tsvetovat, 2004, “An Integrated Approach to the Collection and Analysis of Network Data,” In Proceedings of the NAACSOS 2004 Conference, Pittsburgh, PA, electronic publication: http://www.casos.cs.cmu.edu/events/conferences/2004/2004_proceedings/Carley_Diesner_Reminga.pdf
Carley, K. M., & Kaufer, David S (1993). Semantic Connectivity: An Approach for Analyzing Symbols in Semantic Networks. Communication Theory, 3(3), 183-.
Carley, K. M., & 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.
Chow-White, Peter A. (2006) “Race, gender and sex on the net: semantic networks of selling and storytelling sex tourism.” Media, Culture & Society. 28(6): 883-905. http://mcs.sagepub.com/cgi/reprint/28/6/883
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.
Contractor, Noshir S.; Ehrlich, Matthew C. (1993). Strategic Ambiguity in the Birth of a Loosely Coupled Organization: The Case of a $50-Million Experiment. Management Communication Quarterly, 6(3), 251-.
Contractor, Noshir S.; Seibold, David R.; Heller, Mark A. (1994). Interactional Influence in the Structuring of Media Use in Groups: Influence in Members' Perceptions of Group Decision Support System Use. Human Communication Research, 22(4), 451-.
Corman, S. R., Kuhn, T., McPhee, R. D., & Dooley, K. J. (2002). Studying Complex Discursive Systems: Centering Resonance Analysis of Communication. Human Communication Research, 28(2), 157-206.
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. A. (1982). A network-based content analysis methodology for computer-mediated communication: An illustration with a computer bulletin board, in M. Burgoon (Ed.), Communication Yearbook 5 (pp. 904-925). New Brunswick, NJ: Transaction Books.
Danowski, J. A. (1988). Organizational infographics and automated auditing: Using computers to unobtrusively gather and analyze communication. In G. Goldhaber and G. Barnett (eds.) Handbook of organizational communication (pp. 335-384). Norwood, NJ: Ablex.
Danowski, J., & Edison-Swift, P. (1985). Crisis effects on intraorganizational computer-based communication. Communication Research, 12, 251-270.
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.
Danowski, J.A. (1993b). Network analysis of message content. G. Barnett, & W. Richards (eds.). Progress in communication sciences XII (pp. 197-222). Norwood, NJ: Ablex.
Danowski, J.A. (2007a). Nodetric. [Computer program]; Chicago, IL: University of Illinois at Chicago.
Danowski, J.A. (2007b). Z-Word & Z-Pair. [Computer programs]: Chicago, IL: University of Illinois at Chicago.
Danowski, J.A. (2008a). WordLink Infinity. [Computer program]: Chicago, IL: University of Illinois at Chicago.
Danowski, J.A. (2008b). OptiComm. [Computer program]: Chicago, IL: University of Illinois at Chicago.
Danowski, J.A. (2008c). Network analysis of message content. In K. Krippendorff & M. Book (eds.) The content analysis reader. Thousand Oaks: Sage Publications.
Diesner, J. and Carley, K. M. 2004, “Using Network Text Analysis to Detect the Organizational Structure of Covert Networks,” In Proceedings of the NAACSOS 2004 Conference, Pittsburgh, PA, electronic publication: http://www.casos.cs.cmu.edu/events/conferences/2004/2004_proceedings/Diesner_Carley.pdf
Diesner, J., & Carley, K. M. (2005). Revealing Social Structure from Texts: Meta-Matrix Text Analysis as a novel method for Network Text Analysis. In V. K. Narayanan & D. J. Armstrong (Eds.), Causal Mapping for Information Systems and Technology Research: Approaches, Advances, and Illustrations (pp. 81-108). Harrisburg, PA: Idea Group Publishing.
Diesner, J., & Carley, K. M. (2008). Conditional Random Fields for Entity Extraction and Ontological Text Coding. Journal of Computational and Mathematical Organization Theory, 14, 248 - 262.
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., & Fitzgerald, G. A. (2004). A case study of cooperation in a commission-based organization. Communication Studies, 55, 553-568.
Doerfel, M. L., & Marsh, P. S. (2003). Candidate-issue positioning in the context of presidential debates. Journal of Applied Communication Research, 31, 212-237.
Doerfel, M.L. & G.A. Barnett, A Comparison of the Semantic and Affiliation Networks of the International Communication Association. Human Communication Research, 25 (4), 589-603, 1999.
Fitzgerald, G. A.; Doerfel, M. L. (2004). The Use of Semantic Network Analysis to Manage Customer Complaints. Communication Research Reports, 21(3), 231-242.
Flores-d'Arcais, G.B., & Schreuder, R. (1987). Semantic activation during object naming. Psychological Research, 49, 153-159.
Franzosi, Roberto. 1989. “From Words to Numbers: A Generalized and Linguistics-Based Coding Procedure for Collecting Textual Data.” Sociological Methodology 19: 263-298.
Franzosi, Roberto. 1990. “Computer-Assisted Coding of Textual Data: An Application to Semantic Grammars.” Sociological Methods and Research 19(2): 225-257.
Fuentes, Luis J.; Vivas, Ana B.; Humphreys, Glyn W. (1999). Inhibitory Mechanisms of Attentional Networks: Spatial and Semantic Inhibitory Processing. Journal of experimental psychology, 25(4), 1114.
Gloor, P., & Zhao, Y. (2006, July 2006). Analyzing actors and their discussion topics by semantic social network analysis. Paper presented at the 10th IEEE International Conference on Information Visualisation London.
Golbeck, J.; Hendler, J. (2004). Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based Social Networks. Lecture Notes in Computer Science, 3257, 116-131.
Gruzd, A. (2009). Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog. Proceedings of the Internet Research 10.0 Conference, October 7-11, 2009, Milwaukee, WI, USA. Available at http://anatoliygruzd.com/pub/gruzd_aoir_network_discovery.pdf
Gruzd, A. (2009). Studying Collaborative Learning using Name Networks. Journal of Education for Library and Information Science (JELIS) 50(4): 243-253. Available at http://anatoliygruzd.com/pub/gruzd_2009_jelis_name_networks.pdf
Gruzd, A. A. (April 1, 2009). Automated Discovery of Social Networks in Online Learning Communities. Doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL. Available at http://anatoliygruzd.com/pub/gruzd_anatoliy.pdf
Gruzd, A. and Haythornthwaite, C. (2008). Automated Discovery and Analysis of Social Networks from Threaded Discussions. Paper presented at the International Network of Social Network Analysts, St. Pete Beach, FL, USA. Available at http://tinyurl.com/3xsdyu.
Hautamaki, A. (1992). A Conceptual Space Approach to Semantic Networks. Computers & mathematics with applications, 23(6/9), 517.
Haythornthwaite, C. and Gruzd, A.A. (2007). A Noun Phrase Analysis Tool for Mining Online Community. In the Proceedings of the 3rd International Conference on Communities and Technologies, Michigan State University. Available at: http://www.iisi.de/fileadmin/IISI/upload/C_T/2007/Haythornthwaite.pdf.
Heald, Maureen R.; Contractor, Noshir S.; Wasserman, Stanley. (1998). Formal and Emergent Predictors of Coworkers' Perceptual Congruence on an Organization's Social Structure. Human Communication Research, 24(4), 536-.
Jang, H. & G.A. Barnett, Cultural Differences in Organizational Communication: A Semantic Network Analysis. Bulletin de Methodologie Sociologique, 44, 31-59, 1994.
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 Lang.
Kang, N., & Choi, J. H. (1999). Structural implications of the crossposting network of international news in cyberspace. Communication Research, 26(4), 454-481.
Kapucu, Naim, Maria-Elena Augustin and Vener Garayev. (2009). *Interstate Partnerships in Emergency Management: Emergency Management Assistance Compact (EMAC) in Response to Catastrophic Disasters,* Public Administration Review. Volume 69 (2): 297-313.
Kaufer, D. S. and Kathleen M. Carley, 1993, “Condensation Symbols: Their Variety and Rhetorical Function in Political Discourse,” Philosophy and Rhetoric, 26(3): 201-226.
Kaufer, D. S. and Kathleen M. Carley, 1993, Communication at a Distance: The Effect of Print on Socio-Cultural Organization and Change,Hillsdale, NJ: Lawrence Erlbaum Associates.
Lebeth, K. (2001). Semantic Networks in a Knowledge Management Portal. Lecture Notes in Computer Science, 2174, 463-466.
Leifeld, Philip, and Sebastian Haunss (2010), A Comparison between Political Claims Analysis and Discourse Network Analysis: The Case of Software Patents in the European Union, Preprints of the Max Planck Institute for Research on Collective Goods Bonn 2010/21, Bonn: Max Planck Institute for Research on Collective Goods [available at http://www.coll.mpg.de/pdf_dat/2010_21online.pdf].
Li, Y.; Bandar, Z.; Mclean, D. (2002). Measuring Semantic Similarity Between Words Using Lexical Knowledge and Neural Networks. Lecture Notes in Computer Science, 2412, 111-116.
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.
McArthur, R. and Bruza, P.D. (2003) Discovery of tacit knowledge and topical ebbs and flows within the utterances of online community. Chapter in Chance Discovery, Ohsawa, Y. and McBurney, P. (Eds), Springer-Verlag. pp115-132.
Mesina, M.; Roller, D.; Lampasona, C. (2004). Visualisation of Semantic Networks and Ontologies Using AutoCAD. Lecture Notes in Computer Science, 3190, 21-29.
Michaelson, Alaina; Contractor, Noshir S. (1992). Structural Position and Perceived Similarity. Social Psychology Quarterly, 55(3), 300-.
Mohr, J. Measuring Meaning Structures. 1998. Annual Review of Sociology. 24: 345-70.
Monge, Peter R.; Cozzens, Michael D.; Contractor, Noshir S. (1992). Communication and Motivational Predictors of the Dynamics of Organizational Innovation. Organization Science, 3(2), 250-. 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.
Oliver A.L. and K. Montgomery. 2008. Using Field Configuring Events for Sense Making: A Cognitive Network Approach, Journal of Management Studies, 45:6, 1147-1167.
Oliver, A.L. and K. Montgomery. 2005. Toward the construction of a profession's boundaries: Creating a networking agenda. Human Relations, 58:1167-1184. Oliver, A.L. and M. Ebers. 1998. Networking Network Studies - An Analysis of Conceptual Configurations in the Study of Inter-Organizational Relations. Organization Studies, 19, 4:549-583
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
Palmquist, M., Carley, K M., and Dale, T. 1997, “Two applications of automated text analysis: Analyzing literary and non-literary texts,” Chapter 10 in C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Transcripts. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 171-189.
Paolillo, J. C. (2001). Language variation on Internet Relay Chat: A social network approach. Journal of Sociolinguistics, 5(2), 180-213.
Popping, R (2003) Knowledge Graphs and Network Text Analysis, Social Science Information 42 (1): 91-106.
Popping, R. http://ics.uda.ub.rug.nl/FILES/root/Articles/2003/PoppingR-Knowledge/PoppingR-Knowledge-RUG-2003.pdf
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.
Rice, R. E., Chapin, J., Pressman, R., Park, S., & Funkhouser, E. (1996). What's in a name? Bibliometric analysis of 40 years of the Journal of Broadcasting (and Electronic Media). Journal of Broadcasting and Electronic Media, 40, 511-539.
Roberts, C. W. & Popping, R. (1993). Computer-Supported Content Analysis: Some Recent Developments. Social Science Computer Review, 11(3), 283-291.
Roberts, C. W. (2000). A Conceptual Framework for Quantitative Text Analysis: On Joining Probabilities and Substantive Inferences about Texts. Quality and Quantity, 34(3), 259-274.
Roberts, C. W. A Generic Semantic Grammar for Quantitative Text Analysis: Applications to East and West Berlin Radio News Content from 1979. Sociological Methodology, vol. 27, pp. 89-129, 1997.
Sauzeon, H.; Lestage, P.; Raboutet, C.; N'Kaoua, B.; Claverie, B. (2004). Verbal fluency output in children aged 7-16 as a function of the production criterion: Qualitative analysis of clustering, switching processes, and semantic network exploitation. Brain and Language, 89(1), 192-202.
Savoy, J. (1992). Bayesian inference networks and spreading activation in hypertext systems. Information Processing and Management, 28, 389-406.
Shapiro, M. A.; Lang, A.; Hamilton, M. A.; Contractor, N. S. (2001). Information Systems Division: Intrapersonal, Meaning, Attitude, and Social Systems. Communication Yearbook, 24, 17-50.
Sherblom, J. C.; Reinsch, N. L.; Beswick, R. W. (2001). Intersubjective Semantic Meanings Emergent in a Work Group: A Neural Network Content Analysis of Voice Mail. Progress in Communication Sciences, 17, 33-50.
Shtyrov, Y.; Hauk, O.; Pulvermuller, F. (2004). Distributed neuronal networks for encoding category-specific semantic information: the mismatch negativity to action words. European Journal of Neuroscience, 19(4), 1083-1092.
Smith, A., & Humphreys, M. (2006). Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping. Behavior Research Methods, 38(2), 262-279.
Smith, M. (1999). 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.
Smith, T. Narrative boundaries and the dynamics of ethnic conflict and conciliation. Poetics 35:1, Pages 22-46, 2007.
Stohl, Cynthia. (1993). European Managers' Interpretations of Participation: A Semantic Network Analysis. Human communication research, 20(1), pp. 97.
Ulicny, B. Mieczyslaw M. Kokar, Christopher J. Matheus, “Metrics For Monitoring A Social-Political Blogosphere: A Malaysian Case Study,” IEEE Internet Computing, vol. 14, no. 2, pp. 34-44, Mar./Apr. 2010, Special Issue on Social Computing in the Blogosphere. doi:10.1109/MIC.2010.22
Van Atteveldt, W. (2008). Semantic network analysis: Techniques for extracting, representing, and querying media content. Charleston, SC: BookSurge Publishers.
White, K.R. & Dandi, R. (2009). Intrasectoral Variation in Mission and Values: The Case of Catholic Health Care Systems. Health Care Management Review 34(1): 68-79
Woelfel, J. (1991). CatPac [Computer program]. Buffalo, NY: New York State University, Department of Communication.
Yates, J. and Orlikowski, W. J. (1992). Genres of Organizational Communica- tion: A Structural Approach to Studying Communication and Media. Academy of Management Review, 17(2):299-326.
Young, Michael D. (1996). Cognitive Mapping Meets Semantic Networks. Source: Journal of conflict resolution, 40(3), 395-414.
Zelenko, D., Aone, C., & Richardella, A. (2003). Kernel methods for relation extraction. The Journal of Machine Learning Research, 3, 1083-1106.
. . . . . . . . . . . . . . . . . . . . . . . . . . .
Ronald E. Rice
Arthur N. Rupe Chair in the Social Effects of Mass Communication
Co-Director, Carsey-Wolf Center for Film, Television, and New Media
President of the International Communication Association 2006-2007
Dept. of Communication, 4005 Social Sciences & Media Studies Bldg.
University of California
Santa Barbara, CA 93106-4020
Ph: 805-893-8696; Fax: 805-893-7102
rrice@comm.ucsb.edu
http://www.comm.ucsb.edu/people/faculty/rice.php
http://www.cftnm.ucsb.edu/
Current Folder: INBOX Sign Out Compose Addresses Folders Options Search Help Message List | Delete Previous | Next Forward | Forward as Attachment | Reply | Reply All Subject: [bms-rc33] Call - Networks in Space 1 Time (19 Sep, Paris) francais (fwd) From: "Barry Wellman" <wellman@CHASS.UTORONTO.CA> Date: Fri, April 5, 2013 14:57 To: SOCNET@LISTS.UFL.EDU Priority: Normal Options: View Full Header | View Printable Version | Download this as a file | Whitelist Sender ***** To join INSNA, visit http://www.insna.org ***** Barry Wellman _______________________________________________________________________ S.D. Clark Professor of Sociology, FRSC NetLab Director Department of Sociology 725 Spadina Avenue, Room 388 University of Toronto Toronto Canada M5S 2J4 twitter:@barrywellman http://www.chass.utoronto.ca/~wellman fax:+1-416-978-3963 NETWORKED:The New Social Operating System. Lee Rainie & Barry Wellman MIT Press http://amzn.to/zXZg39 Print $20 Kindle $16 Old/newCybertimes http://bit.ly/c8N9V8 It's still rock & roll to me ________________________________________________________________________ ---------- Forwarded message ---------- Date: Fri, 5 Apr 2013 13:50:17 +0200 From: Karl.Vanmeter@ens.fr Reply-To: bms-rc33@services.cnrs.fr, Karl.Vanmeter@ens.fr To: bms-rc33@services.cnrs.fr, jourdan@ens.fr Cc: Pierre.Triboulet@toulouse.inra.fr Subject: [bms-rc33] Call - Networks in Space 1 Time (19 Sep, Paris) francais Merci à Pierre Triboulet Date limite: 15 mars 2013 Journée organisée avec le soutien de l'Ecole d'ingénieurs de la ville de Paris, 75018 Paris Présentation de la journée Les dimensions spatiale et temporelle des réseaux font l'objet d'un intérêt croissant de la part de multiples chercheurs et ce depuis une période assez récente. Synthétiser les travaux nombreux et très divers sur ces questions se heurte toutefois au cloisonnement disciplinaire et thématique caractérisant l'analyse de réseau en général. L'objectif de cette journée est donc de rassembler des points de vue divers sur la question du temps et de l'espace dans les réseaux, aussi bien du point de vue des sciences humaines que des sciences de la nature. Quels sont les concepts, méthodes, et outils utilisés pour rendre compte de l'évolution d'un réseau dans le temps et de son intégration à l'espace, et comment ceux-ci peuvent-ils ou doivent-ils être transposés d'une discipline et/ou d'une thématique à une autre ? Un meilleur dialogue entre chercheurs aux spécialités différentes peut-il faire progresser les travaux respectifs, susciter de nouvelles collaborations ? Du point de vue spatial, l'émergence récente du concept de spatial network est à juste titre emblématique d'un intérêt croissant pour la spatialité des réseaux tant en sociologie qu'en sciences physiques. Le rôle et l'influence de l'espace sur l'organisation du réseau restent pourtant peu explicites dans de nombreuses études. Souvent défini comme contrainte en termes de friction ou de coût, l'espace se trouve intégré la plupart du temps via des mesures simples telles que les coordonnées géographiques des sommets et la distance euclidienne. Il serait utile d'intégrer d'autres types de distances (distance perçue par exemple) et de paramètres (frontières, caractéristiques des territoires) dans les modèles et mesures existants. Du point de vue temporel, les recherches sont très diverses et comprennent notamment l'analyse de réseaux à des époques anciennes, la description des propriétés d'un réseau à différents moments de son évolution, la propagation d'un flux sur le réseau, l'étude des graphes dynamiques, la modélisation et la simulation, etc. On peut notamment se poser la question du rôle de l'espace dans l'évolution d'un réseau ainsi que de l'influence de cette évolution sur l'espace. Y a-t-il des trajectoires d'évolution récurrentes d'un réseau à un autre, comment s'expliquent-elles ? Un certain nombre de questions se posent, en effet, quant à la mesure, la description, l�explication, l’exploration et la visualisation des évolutions. Il s'agira moins pour cette journée de proposer des réponses définitives que d'ouvrir des pistes de réflexion afin d'enrichir la compréhension des faits étudiés et d’autoriser les transferts de méthode. La journée n'impose aucune limite disciplinaire ou thématique aux contributions. Quelques pistes sont à envisager même si la liste ci-dessous est loin d'être exhaustive : · distance physique, distance sociale · frontières et territorialités des réseaux · complexité et émergence dans les réseaux · proximité et communautés / clusters · graphes dynamiques, modélisation, simulation, modèles agents Les propositions de communication seront évaluées par le comité scientifique. En cas d'acceptation, un texte long (10 à 12 pages) est impérativement attendu pour le 15 août 2013. L'ensemble des textes sera édité dans la collection Hal-Shs du groupe fmr. Des consignes précises de mise en forme seront envoyées aux auteur-e-s retenu-e-s. Nous encourageons vivement les doctorant-e-s et jeunes docteur-e-s à participer à cette journée. Comité scientifique et d'organisation : Françoise Bahoken, Laurent Beauguitte, Matthieu Drevelle, César Ducruet, Serge Lhomme et Marion Maisonobe. Dates importantes 15 avril : envoi des propositions (1 page maximum) à groupe_fmr@yahoo.fr Début mai : notification aux auteur-e-s 15 août : envoi des textes complets (10 à 12 pages) 15 Septembre: date limite d'inscription Courant octobre 2013: édition des actes de la journée d'étude Consignes pour le résumé Merci d'indiquer précisément titre, mots-clés (3 à 5), nom, prénom, affiliation et mails du ou des auteur- e-s. Étant donné le format demandé, trois à quatre références bibliographiques nous semblent un nombre suffisant. >> Consider submitting your methodologically interesting articles to the BMS << ******************************************************************************** * * Karl M. van Meter BMS, Bulletin de Methodologie Sociologique * karl.vanmeter@ens.fr (Bulletin of Sociological Methodology) * tel 33 (0)1 40 51 85 19 http://bms.sagepub.com/ * École normale supérieure * 48 boulevard Jourdan - 75014 Paris * distribution list bms-rc33@services.cnrs.fr * free distribution list aimsl@services.cnrs.fr * BMS-AIMS RC33, Research Committee Logic & * 45 rue Linné Methodology of the International * 75005 Paris, France Sociological Association (ISA) * http://ens.academia.edu/KarlMvanMeter ******************************************************************************** _____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to listserv@lists.ufl.edu containing the line UNSUBSCRIBE SOCNET in the body of the message.
Subject: Re: Network analysis and law From: "Ryan Whalen" <ryan@ryanwhalen.com> Date: Sat, May 11, 2013 16:54 To: SOCNET@LISTS.UFL.EDU
Hi Erin,
Below are some references that may be of interest. I'm aware of more work dealing with precedent networks in common law countries than semantic networks within statutes, constitutions, or codes, but work on the latter does exist. There is also an upcoming workshop that may interest you titled “Network Analysis in Law” at the International Conference on Artificial Intelligence and Law in Rome next month.
Bommarito, I., Michael, J., Katz, D., & Zelner, J. (2009). Law as a seamless web? Comparison of various network representations of the United States Supreme Court corpus (1791-2005) (pp. 234–235). Barcelona, Spain: ACM.
Boulet, R., Mazzega, P., & Bourcier, D. (2011). A network approach to the French system of legal codes—part I: analysis of a dense network. *Artificial Intelligence and Law*, 1–23.
Cross, F. B., Smith, T. A., & Tomarchio, A. (2006). Determinants of Cohesion in the Supreme Court’s Network of Precedents. *SSRN eLibrary*. Retrieved from http://ssrn.com/paper=924110
Fowler, J. H., Johnson, T. R., Spriggs, J. F., Jeon, S., & Wahlbeck, P. J. (2007). Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court. *Political Analysis*, *15*(3), 324–346. doi:10.1093/pan/mpm011
Katz, D. M., Gubler, J., Zelner, J., Bommarito, M. J., Provins, E. A., & Ingall, E. M. (2009). Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate. *SSRN eLibrary*. Retrieved from http://ssrn.com/paper=1352656
Merkl, D., Schweighoffer, E., & Winiwarter, W. (1999). Exploratory analysis of concept and document spaces with connectionist networks. *Artificial Intelligence and Law*, *7*(2), 185–209. doi:10.1023/a:1008365524782
Thomas, H. F. (2007). The Web of Law. *San Diego L. Rev.*, *44*, 309.
Whalen, R. (2013). Modeling Annual Supreme Court Influence: The Role of Citation Practices and Judicial Tenure in Determining Precedent Network Growth. In *Complex Networks*. Springer Berlin / Heidelberg. Retrieved from http://dx.doi.org/10.1007/978-3-642-30287-9_18
Best,
– Ryan Whalen PhD Student - Media, Technology & Society Northwestern University School of Communication JD Student - Northwestern Law ryanwhalen.com 773.800.0345
On Sat, May 11, 2013 at 5:55 AM, Erin McGrath erin.ertogan@gmail.comwrote:
* To join INSNA, visit http://www.insna.org *
Dear Colleagues:
Can anyone recommend sources on the application of network analysis –
probably semantic network analysis – to law or legal documents?
In particular, I am interested in constitutional systems, for which I have
found some very interesting theoretical work from lawyers (Adrian Vermeule)
and political scientists (Jenna Bednar). I am looking more for applications
and methodological help.
Thanks in advance and best wishes,
Erin McGrath
Attachments: untitled-[2] 11 k [ text/html ] Download | View