NiCHE Social Network Analysis and Visualization

fig5nichetwitterfollowers20110421In April 2008, I posted an article in my blog Digital History Hacks about visualizing the social network of NiCHE: Network in Canadian History & Environment as it was forming. We now use custom programs written in Mathematica to explore and visualize the activities of NiCHE members, and to assess our online communication strategies. Some of the data comes from our online directory, where members can contribute information about their research interests and activities. Some of it comes from our website server logs, and some of it is scraped from social networking sites like Twitter. A handful of examples are presented here, but the possibilities for this kind of analysis are nearly unbounded.

Clusters of research interest

Clusters of research interest

When NiCHE members add their information to our online directory, they are encouraged to select one or more of a set of research interests. This heat map shows the degree of overlap for each pair of topics, with brighter colours indicating a greater number of people who indicated an interest in both areas. Some of the pairings are not surprising: people who are interested in landscape are often interested in conservation, environmentalism and parks, and vice versa. The absence of overlap is also meaningful. People who are interested in fisheries seem not to be interested in landscape, and vice versa. Why not? A workshop that tried to bring both groups together to search for common ground might lead to new insights. Studying visualizations like this one also allow us to assess the extent to which our original thematic projects (focusing on Landscapes, Forest History, Water, etc.) actually cover the interests of members. Some of the members of the NiCHE executive do research in the history and philosophy of science, and this is apparently something that many NiCHE members are also interested in. A future workshop to address this interest might be co-hosted by NiCHE and the Situating Science knowledge cluster.

Research Interest Overlaps.

Research Interest Overlaps.

Looking at the same information in a different way brings new things to light. This figure shows the degree of overlap between pairs of research interests as a graph rather than a heat map. Research topics are represented as vertices, and the size of the edge connecting each pair indicates the degree of overlap. This graph suggests that NiCHE members who are interested in subjects that focus on material evidence over very long temporal durations are relatively marginal in the knowledge cluster, and may not be well connected even with one another. Again, being able to visualize the data gives us the possibility of addressing the situation. Perhaps we should make more outreach to geologists and archaeologists?

Bridging Capital

Bridging Capital

Studies of social networks suggest that their “small world” properties are typically due to people who provide bridges between interest groups or make other kinds of long-distance connections. Here we use a graph to visualize every pair of research topics that are of interest to a single NiCHE member. From this figure it is easy to see that Darin Kinsey is the only person who has claimed to be interested in both landscapes and fisheries. If we did decide to hold a workshop on the intersection of those two topics, he might be the ideal person to help organize it. If we want to try to get scholars talking to one another across methodological or thematic boundaries, then we should enlist the help of people like Ravi Ranganathan, Norm Catto and Liza Piper to get the conversation started.

Centrality of NiCHE activities

Centrality of NiCHE activities

Our online directory also allows NiCHE members to indicate which activities they have participated in. The vertices in this graph represent NiCHE members and activities that we have sponsored. If a member participated in a particular activity, there is an edge connecting the two vertices. The color of each vertex represents a measure of network centrality. Here we have labeled the most central of our activities. Note that the conferences (especially Confluences 2007 and Climate History 2008) drew relatively large numbers of participants who did not attend other NiCHE activities. Participants in the summer field schools (CHESS), on the other hand, were much more likely to attend more than one of our activities. This suggests that our field schools do a better job of helping to constitute NiCHE as an ongoing entity than regular conferences would. This is consistent with reports that we have received from regular CHESS participants, especially new scholars.

NiCHE Twitter Followers.

NiCHE Twitter Followers.

In April 2011, NiCHE had about 340 followers on the social networking site Twitter. Each vertex in this graph represents one Twitter user. The size of the icon is scaled according to the log of the number of followers that each user has. (In this case, the number of followers range from a handful to tens of thousands, depending on the user). The edges of the graph represent some of the connections between Twitter users who follow one another. This figure shows that the NiCHE Twitter audience includes a relatively dense network of scholars who identify themselves either as digital humanists or as Canadian / environmental historians or geographers. There is also a relatively large collection of followers who do not appear to have many connections with one another. Knowing something about who is reading our tweets enables us to gauge the degree to which our online knowledge mobilization activities are effective, and helps us think about targeting our messages to particular communities.

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William J. Turkel

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