Analyzing Social Media Networks with NodeXL Derek Hansen, Cody Dunne, and Ben Shneiderman

Social interactions are increasingly mediated or augmented by technology. A variety of social media platforms exist, helping to support the varied needs of individuals, organizations, and communities. However, the social and technical challenges of successfully using social media to reach desired ends are far from trivial. For every Wikipedia there are hundreds of failed wikis scattered across the web. There is a great need for understanding what happens in social media and what actions lead to successes and failures.

Patterns are left behind One key characteristic of technology-mediated communication is that it can (and typically does) capture detailed data on social interactions. Just like footprints left on the sand tell a story about walking a dog on the beach, our digital footprints tell stories about our online behaviors and interactions. The mass of data created by social media has the potential to usher in a golden age of social science and data-driven decision making. However, to put this social data to good use by researchers, as well as non-technical community managers and decision makers, we need usable and powerful tools that support social media data analysis. Patterns are left behind 3

Research Goal Develop powerful tools, processes, and methods that dramatically lower the barriers for community managers and researchers to make sense of social media interactions. Thus, our goal is to…

Online Community Analysis A few pioneering companies and researchers are beginning to develop metrics and tools that enable individuals to analyze community interaction in more meaningful ways. Nearly all such systems are based on participation statistics such as how often people post and how long they stick around. While these are important metrics that can be aggregated in meaningful ways and tracked over time, they miss perhaps the most important dimension of social media: namely social connections. They fail to capture or analyze “relational data” such as “who is friends with who” or “who has influenced who” Fortunately, there is a robust set of concepts and mathematical language for dealing with relational data. It is called Social Network Analysis.

Social Network Analysis A systematic method for understanding relationships between entities. Vertex-Specific Metrics Betweenness Centrality Degree Centrality Eigenvector Centrality Closeness Centrality Network-Specific Metrics Components Density Social network analysis sees the world as consisting of entities (called vertices or nodes) and connections between those entitles (called edges or ties). It lends itself to visual representations such as the one you see, in the form of a network “graph”. A set of metrics can be calculated to characterize the network as a whole or individual entities within the network. For example, in this simple “kite friendship network”, Diane has the highest “Degree Centrality”, which is a fancy way of saying she has the most friends. However, Heather has the highest “betweenness centrality” suggesting her important spot as a bridge spanner between Jane and Ike and the rest of the group. The point here is that social network analysis provides a compelling method to understanding social media data. Unfortunately, it has until recently only been used by those with PhDs or those in specialized fields such as intelligence analysts.

Research Question (1) How can the complex, sophisticated set of SNA techniques be supported in an intuitive manner for community analysts? To help Social Network Analysis accessible to a less technical set of users such as community analysts, we have been addressing the following question through the development and testing of a novel SNA analysis tool called NodeXL.

This work is funded by Microsoft Research and performed by a network of collaborators including many in this room, as well as distant collaborators.

NodeXL (http://nodexl.codeplex.com) What you see is a screenshot from the NodeXL tool. NodeXL is a plugin (technically a template) for Microsoft Excel 2007 and above, which allows you to analyze and visualize relational data. It includes features that make it easy for analysts to grab data from social media tools like email, Twitter, YouTube, and Flickr; and then make sense of that data by calculating metrics and visualizing networks. It is constantly being updated to improve scalability, usability, and functionality, and serves as a platform on which to try novel approaches to SNA. Attribute data or network metric calculations describing individuals and connections in the network can be mapped onto different visual attributes such as size, color, and opacity. Subgraph images like those seen on the left characterize person-specific networks and Excel’s formulas can be used to calculate additional metrics. For example, this network shows the most active contributors to a website design Q&A community, with greener nodes filling the social role of “question answerer” and redder nodes representing discussion starters.

Research Question (2) How do non-technical users learn SNA and apply it to understand community interaction? What barriers do they encounter? How should SNA tools be customized for novices? We have been using NodeXL in courses for non-technical students unfamiliar with social network analysis to better understand the process they go through, the barriers they encounter at various steps in that process, and the ways that SNA tools can be customized to meet their particular needs. For example, we found that the tight integration of the visual network browser and spreadsheet data was key for students’ understanding. We also found the need to support the data collection and structuring phase, and improve layouts. These and other detailed findings have led to improvements in NodeXL.

Finding a New Administrator This is an example of a student assignment who applied NodeXL to provide data that would help in the selection of a new community administrator. Nodes that are larger and darker score highly on SNA centrality metrics, helping draw attention to possible candidates. Many other compelling examples were created by students in my Communities of Practice class after only a 3-week module on SNA and NodeXL.

Research Question (3) How can SNA be applied to different social media platforms to gain actionable insights? Another line of research is focused on developing methods and visualizations that help analysts gain actionable insights related to their participation in specific social media platforms. Myself, Ben Shneiderman, and Marc Smith have a book coming out in September (with several collaborators, some of whom are here) that helps address this question.

http://nodexl.codeplex.com Forthcoming, Sept 2010 The book introduces social media and social network analysis and then uses NodeXL to demonstrate how to analyze and visualize various social media networks such as email, forums, Twitter, Facebook, website hyperlinks, Flickr, YouTube, and Wikis. In my remaining couple of minutes I’ll show you a few examples. Forthcoming, Sept 2010 http://nodexl.codeplex.com

Personal Email Collection This is an example of my personal email network for a month of time, where edges represent messages sent from person to person. Notice that I’m not in it, since that would only increase clutter. Visualizations like this one help identify clusters (like the NodeXL team in the center who was working on a paper this month, or my family) and social roles such as my grandparents who send messages to many people (namely my distant cousins). As a professor I connect directly with many students who don’t copy in others (note the many isolates on the left). Taken as a whole, this image shows a social fingerprint of my interactions. Similar graphs for others, such as those in organizations display different patterns of communication.

Mapping Corporate Email Communication Between Research Groups In this corporate email network, vertices represent organizational units within a large company and edges represent email connections between them. Red squares are research units and blue ones are non-research units that interact with research units. As with many graphs of large networks, this one was filtered to remove connections below a certain threshold. Those familiar with the company can use this network to identify which organizations are not communicating with others, as well as identify units that play important bridge spanning roles.

Who is Talking about Kodak on Twitter? This is a network graph of Twitter users who mentioned the word Kodak in a tweet. Larger vertices post more often. Graphs like this can help identify opinion leaders and distinct subgroups of participants.

Finding Friendship Clusters in Facebook? This network graph of Bernie Hogan’s Facebook friends lays out the vertices into meaningful clusters based on shared network ties. From it you can identify bridge spanners and get a sense of which of Bernie’s friendship clusters are most closely linked together.

Building Fan Communities on YouTube Finally, these two graphs created by Dana Rotman and Jen Golbeck compare how fans of two musicians engage with each other via YouTube. The one on the left has a low density compared to the one on the right, which has a high density suggesting a strong sense of community between fans.

Conclusion There is a pressing need to support community managers and researchers trying to make sense of social media data. We have shown that non-technical students can learn SNA and apply it to online community interactions using NodeXL with minimal support. We have begun to create a pallet of network visualizations for specific social media networks, which can be used to gain actionable insights. At a high level, our work can be summarized as follows…

Analyzing Social Media Networks with NodeXL Derek Hansen, Cody Dunne, and Ben Shneiderman Thanks to Microsoft Research Natasa Milic-Frayling Dan Fay Our HCIL Collabortors: Dana Rotman Elizabeth Bonsignore Udayan Khourana Puneet Sharma The NodeXL Team A special thanks to Microsoft Research, our other HCIL collaborators, and the NodeXL team. http://nodexl.codeplex.com