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Great comments and suggestions thus far!

Also relevant to this discussion is a book chapter that Caroline Haythornthwaite and I have recently published that deals with issues around social network discovery from textual data on the Internet:
 * Gruzd, A. & Haythornthwaite, C. (2011). Networking Online: Cyberсommunities. In Scott, J. & Carrington, P. (Eds.), Handbook of Social Network Analysis. London: Sage, pp. 449-487.
Also we've been experimenting with what we call 'Name Networks' - a network discovery method based on examining the content of each message to identify any personal names (and/or usernames) to connect people based on the 'Who-Mentions-Whom' or 'Who-is-CoMentioned-With-Whom' principle. Discovered personal names have shown to be good indicators of the actual addressee(s) of the message as well as good indicators of closer social connections among network members. Here are some papers that relied on this method to study online communities:
* Gruzd, A., and Sedo, D.R. (2012) #1b1t: Investigating Reading Practices at the Turn of the Twenty-first Century. Journal of Studies in Book Culture, Special issue on New Studies in the History of Reading, 3(2). doi: 10.7202/1009347ar  http://www.erudit.org/revue/memoires/2012/v3/n2/1009347ar.html?vue=integral

* Gruzd, A., Wellman, B., and Takhteyev, Y. (2011). Imagining Twitter as an Imagined Community. American Behavioral Scientist, Special issue on Imagined Communities, 55 (10), 1294-1318, doi: 10.1177/0002764211409378 http://abs.sagepub.com/content/55/10/1294

* Haythornthwaite, C. & Gruzd, A. (2008). Analyzing Networked Learning Texts. Proceedings of Networked Learning Conference, Halkidiki, Greece, May 5-6, 2008, pp. 136-143. http://dalspace.library.dal.ca/handle/10222/12828

* Gruzd, A. (2009). Studying Collaborative Learning Using Name Networks. Journal of Education for Library and Information Science, 50(4), 243-253. (Email me to request a copy)

FYI.. if you are studying online communities, check out Netlytic (http://netlytic.org/); it's a web-based tool that we've been developing here at the Dalhousie Social Media Lab, designed for both text and network analysis of conversational data.

Hope it helps,
Anatoliy
-- 
Anatoliy Gruzd, PhD
Assistant Professor, School of Information Management
Director, Social Media Lab
Faculty of Management / Faculty of Computer Science 
Dalhousie University
Canada

Phone: 902-494-6119
Fax: 902-494-2451
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Research Lab: http://SocialMediaLab.ca
Homepage: http://AnatoliyGruzd.com
Twitter: http://twitter.com/dalprof

On 06/11/2012 2:09 PM, Maksim Tsvetovat wrote:
[log in to unmask]" type="cite">***** To join INSNA, visit http://www.insna.org ***** I've run a fair bit of analyses commercially where we use SNA to map and analyze the *concept space* of social media. 

A few lessons learned:
* Because Tweets are so short, it makes little sense to analyze them in isolation -- larger aggregates work best

* Concept maps can be used to see in-group and out-group relationships -- e.g. how do conservatives refer to liberals and liberals to conservatives? For this analysis, it helps to drop high-degree concepts (e.g. "liberal", "conservative") -- so commonalities and differences stand out

* After dropping high-degree nodes, concept maps can be clustered using any community detection algorithm. 

* Then, go back to users that have tweeted these concepts, and see what *other* concepts they tweet about.

* This forms the basis of an iterative approach -- a sequence of expansions and contractions of the dataset that gets you very close to the range of concepts, topics and people that you're looking for. 

* Finally -- if you're analyzing celebrities or politicians (or anyone with lots of direct speech), concept maps can be used to find legitimators (e.g. concepts used to establish one's place in the community) and differentiation markers (e.g. concepts used to make oneself stand out). 

If you search for me on YouTube, you will find several video lectures where I use this approach to mine election-related tweets and tweets about the revolution in Egypt. 

A detailed paper on this technique is in the works and will be submitted to Social Networks soon.

Max



On Tue, Nov 6, 2012 at 9:11 AM, Thomas Plotkowiak <[log in to unmask]> wrote:
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I've tried to connect people listed for certain topics such as "musician" or "snowboard" into a twitter ontology of interests here:

http://twitterresearcher.wordpress.com/2012/03/16/a-net-of-words-a-high-level-ontology-for-twitter-tags/
http://twitterresearcher.wordpress.com/2012/06/08/how-to-generate-interest-based-communities-part-1/
http://twitterresearcher.wordpress.com/2012/06/12/how-to-generate-interest-based-communities-part-2/

and thought that it might have a  nice application for marketing when a company wants to know what their followers are itnerested in, without actually using some sort of text analysis on their tweets, but instead using their follower ties in this ontology.

http://twitterresearcher.wordpress.com/2012/07/26/finding-out-what-people-are-interested-in-by-using-only-structural-information/

Cheers
Thomas Plotkowiak
Researcher at the MCM Institute St. Gallen



2012/11/6 patrick doreian <[log in to unmask]>
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On 11/6/2012 8:10 AM, Joshua Introne wrote:
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Hi Derek - 

From a somewhat different angle, I've been using Palla's (2007) community evolution algorithm to perform evolutionary topic analysis in teams.  This is intended as a precursor to comparative analysis with social network dynamics.  

A version of our recently accepted CSCW publication on the approach is here:  http://www.academia.edu/2037735/Analyzing_the_Flow_of_Knowledge_in_Computer_Mediated_Teams.

Best,
Josh

Joshua Introne, Ph.D
Research Scientist
Center for Collective Intelligence
Sloan School of Management
Massachusetts Institute of Technology

message.
hi josh,
                the paper looks very interesting. but the link requires access through facebook in order to download it. i do not, and will not join, facebook for privacy reasons. there might be others like me so it would nice if you could send a link that permits real access outside facebook. printing from the link is impossible.
with best wishes
pat


-- 
patrick doreian
professor emeritus
department of sociology
2602 WWPH
university of pittsburgh
pittsburgh, PA 15260
phone: 412 648 7537
fax: 412 648 2799
website: http://patrickdoreian.com
Social Networks
http://ees.elsevier.com/son/default.asp
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