NodeXL: Social Network Analysis for Scholars

[This is a guest post by Lisa Rhody, who works for the Roy Rosenzweig Center for History and New Media at George Mason University as the project manager for WebWise 2013.

She is currently working on a project called Revising Ekphrasis, which uses advanced computational tools to explore connections between 4,500 English-language poems. You can find her online at LisaRhody.com and follow her on Twitter at @lmrhody.]

From early posts about scholarly uses of social media to more recent entries on its usefulness for improving student engagement, there seems to be a general consensus among ProfHacker writers that the use of social media promotes the widening of scholarly networks. Keeping in mind that online social networks extend beyond the obvious Twitter and Facebook—blogs, podcasts, wikis, and photo/video sharing sites are a few other forms of social media—the vexing question to answer has been how to quantify the scope or significance of one’s participation in social media to a wider scholarly conversation.

Even though social network analysis as a methodology has been around since the 1950s, interactions within computer mediated communities (CMCs) are more easily imported, calculated, and analyzed because they are born-digital. As social media usage increases in popularity, collecting and computing social networks has fuelled the explosion of a new field of social science research and, correspondingly, software packages to help even novices begin to analyze their own online networks.

Why would someone who isn’t a sociologist want to analyze their online social networks?

Scholars might be interested in analyzing social media networks for a number of reasons:

• For those who argue that their social media participation should be represented among their contributions to scholarship, a method for quantifying their impact on and prestige within a scholarly, online network could add a new dimension to the conversation.
• Understanding the shape and composition of one’s network helps to target ways to expand or reshape online communities. For example, you might discover that your network is composed primarily of a single demongraphic. That knowledge would allow you to be more thoughtful about expanding your scholarly connections to include a more diverse network.
• Conferences frequently use social media, and in particular Twitter, to create backchannel conversations. Social network analysis of conference hash tags can draw into relief whether or not that conversation reflects a similar face-to-face experience. It can help conference organizers better understand their audience and show whether or not what is going on at the conference is expanding outward to a broader community.

Consider, for example, the debate over the usefulness of a backchannel Twitter conversation at conferences, such as the Modern Languages Association Annual Convention. On the one hand, some would-be conference goers, like Brian Croxall in 2009 and J.J. Cohen in 2012, found Twitter to be to their advantage because they were unable to attend the conference in Seattle. On the other hand, this year Ryan Cordell and Kathleen Fitzpatrick were prompted to ask how Twitter might also create exclusive groups and degrade scholarly discourse. Social network analysis sheds additional light onto these issues, but from an entirely quantitative perspective.

What can you use to analyze your own social network?

There are, actually, quite a few tools available to use to analyze social media networks. Gephi is one of them; however, NodeXL—a free, open-source template developed for use with Microsoft® Excel® 2007 and 2010 to visualize and analyze social media networks— is the most attractive option for the average user. NodeXL has been integrated into a familiar software environment (Excel), which makes it easier to learn and to use. It imports data directly from many social media applications, which makes manipulating your data less cumbersome.

Figure 1 (click to enlarge): NodeXL is free, open, and downloadable.

Supported by the Social Media Research Foundation, NodeXL offers an active, online forum for support. Its relationship to the Social Media Research Foundation means that it will remain an open tool, designed to produce open data and open scholarship, as exemplified by the NodeXL Gallery, a data and graph-sharing repository.

In order to use NodeXL, you need to be able to run Windows 7, Vista, or XP on your machine. Download the template from its Codeplex site, and unzip it in any folder. After you unzip the folder, open the Setup.exe file. NodeXL will automatically install the templates in the correct locations. Since it is a template and not a stand-alone application, to open NodeXL for the first time, you need to go to your Start menu in Windows, to choose “All Programs,” and to select NodeXL Template to get started.

The primary developers of NodeXL—Derek L. Hansen, Ben Shneiderman, and Marc Smith—have published a book available in print and Kindle editions called Analyzing Social Media Networks with NodeXL: Insights from a Connected World, which is a helpful, step-by-step guide to importing, calculating, and visualizing technology-mediated social networks. While it may not answer how profoundly one’s social media presence affects popular discourse, social network analysis in general and NodeXL in particular can be a powerful tool for understanding better individual scholarly networks, as well as communities of online, scholarly conversation.

Have you considered finding ways to analyze your computer-mediated social network or the online network of an organization you are familiar with? What tools have you used? What would you like to learn?