Numbers, Facts and Trends Shaping Your World

Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters

Appendix: How to analyze social media networks

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Resources and references

Adamic, Lada A, and Natalie Glance. “The political blogosphere and the 2004 US election: divided they blog.” Proceedings of the 3rd international workshop on Link discovery 21 Aug. 2005: 36-43.

Early work documenting polarization in social media through an analysis of networks of blog connections.

Andrei Broder, Ravi Kumar, Farzin Maghoul, Prabhakar Raghavan, Sridhar Rajagopalan, Raymie Stata, Andrew Tomkins, Janet Wiener, Graph structure in the web,  Computer Networks 33, 1 (2000), 309-320

An influential paper showing a “bow-tie” structure to the World Wide Web.

Cailloux, O., Lamboray, C., and Nemery, P. A taxonomy of clustering procedures, Proc. 66th Meeting of the EURO Working Group on Multiple-Criteria Decision Aiding, Marrakesh, Morocco, 2007. Available at

This paper describes multi-criteria clustering procedures and offers a taxonomy to guide further research.

Easley, D. and Kleinberg, J. Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, NY, 2010.

Thorough and lucid textbook on network analysis and applications. Great starting point, yet filled with deep insights for all readers.

Fuehres, H., Fischbach, K., Gloor, P. A., Krauss, J., and Nann, S., Adding taxonomies obtained by content clustering to semantic social network analysis, On Collective Intelligence, Advances in Intelligent and Soft Computing, 76, 2010. Available at:

This paper includes textual content analysis of terms used in social networks to form clusters in communities.

Gleave, Eric, Howard Welser, Thomas Lento, and Marc Smith. 2009. “A Conceptual and Operational Definition of “Social Role” in Online Community.” The 42nd Hawaii International Conference of System Sciences.

This paper applies the idea of social roles to the distinctive network patterns that form around each individual.

Hansen, D., Shneiderman, B., and Smith, M.A., Analyzing Social Media Networks with NodeXL: Insights from a Connected World, Morgan Kaufmann Publishers, San Francisco, CA, 2011.

An introduction to social network analysis followed by a guide to NodeXL, with extensive application examples for mapping Twitter, Facebook, YouTube, flickr, Wikis, blogs, email and more.

Hansen, D.; M.A. Smith, and B. Shneiderman. 2011. “EventGraphs: Charting Collections of Conference Connections.” 44th Hawaii International Conference on System Sciences (HICSS).

This paper social media network analysis to map and understand the shape of conference hashtags in Twitter.

Himelboim, Itai, McCreery, Stephen, Smith, Marc, 2013. Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter, Journal Computer-Mediated Communication 18(2), 40-60. DOI: 10.1111/jcc4.12001

This paper documents the ways political discussions form homogenous clusters of like-minded people.

Himelboim, I.; Gleave, E.; Smith, M.; , Discussion catalysts in online political discussions: Content importers and conversation starters, Journal of Computer Mediated Communication, 14, 4, 771-789, 2009, Wiley Online Library.

This paper documents the ways political discussions are led by a small number of influential participants.

Nishikawa, T. and Motter, A. E., Discovering network structure beyond communities, Nature Scientific Reports 1, Article number: 151, 2011. Available at: doi:10.1038/srep00151

This paper takes a fresh approach to finding substructures inside communities, identifying groups with meaningful properties through interactive visual analytics strategies. It also has a thorough review of previous work.

NodeXL: Network Overview, Discovery and Exploration for Excel:

The distribution and support site for NodeXL.

NodeXL Graph Gallery:

The archive of shared NodeXL network data sets and visualizations.

Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Defining and identifying communities in networks. Proc. National Academy Sciences 101, 2004, 2658–2663.

Offers a strategy for finding community structures in large networks. Their algorithm is fast and scalable.

Rodrigues, Eduarda Mendes, Natasa Milic-Frayling, Marc Smith, Ben Shneiderman, Derek Hansen, Group-in-a-box Layout for Multi-faceted Analysis of Communities. IEEE Third International Conference on Social Computing, October 9-11, 2011. Boston, MA

A paper outlining the utility of Group-in-a-box display for social network analysis.

Smith, Marc, Derek Hansen, and Eric Gleave. 2009. “Analyzing Enterprise Social Media Networks.” In SCA09: Proc. International Symposium on Social Computing Applications. IEEE Computer Society Press.

This paper applies social network analysis to the social media connections created within large organizations.

Smith, M., B. Shneiderman, N. Milic-Frayling, E.M. Rodrigues, V. Barash, C. Dunne, T. Capone, A. Perer, and E. Gleave. 2009. “Analyzing (Social Media) Networks with NodeXL.” Pp 255-264 in C&T ’09: Proc. Fourth International Conference on Communities and Technologies. New York, NY: ACM.

A guide to NodeXL.

Welser, H.T., D. Cosley, G. Kossinets, A. Lin, F. Dokshin, G. Gay, and M. Smith. 2011. “Finding Social Roles in Wikipedia.” Pp 122-129 in Proceedings of the 2011 iConference. ACM.

This paper documents the distinctive patterns of connection created when people perform different roles in Wikipedia.

Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. “Visualizing the Signatures of Social Roles in Online Discussion Groups.” The Journal of Social Structure. 8(2).

 Illustrates different patterns of network structures associated with different kinds of roles and behaviors.

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