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Analysis of Twitter to understand networks of interest and collaboration

The mappings below represent the Twitter traffic generated at two related events this October. These were the Impact Convergence and American Evaluation Society annual conference, one happening immediately after the other and involving many of the same people. Impact Convergence was for people involved or interested in the idea of measuring social value and supporting or financing initiatives on the basis of having measurable social value (my interpretation and wording). The evaluation conference, of course, involved people involved in evaluating the results of policies, programs and investments in mainly social, economic and health/education initiatives. Clearly there is an overlap in interest, and we wanted to see what that might look like. Capturing the Twitter traffic generated by these events was one way to do it.

These mappings were done first with NodeXL, to obtain the data and then process it to produce a set of connections and metrics. This network data was then read into a D3.js file set up to produce an interactive force-directed visual. “Force-directed” means that the points in the mapping are placed relative to each other depending on how strongly and directly or indirectly they are connected – kind of a “gravity” concept.

The first mapping captures all Twitter messages using the official hashtags and handles of both events. The points move around at first as the force-direction calculations take place and converge to a more stable state after 10 or 15 seconds. This is a large network, so not all of it fits in the pane. I have left it this way rather than “squashing” it all in as what is of interest at the moment is the central group connecting both events (I am in that central group, but only because I was regularly sending out updates on the network). A somewhat larger view is available in the “Portfolio” section of this website.

You will see that some nodes (individuals who tweeted or are mentioned in tweets) are larger than others. This is based on “betweenness centrality”, that is, the extent to which one is a “connector” or “bridge” between others. The colours of the nodes denote membership in “clusters”. Members of clusters are relatively more connected as a group to each other than to the rest of the network. Clusters are usually underlain by commonalities among their members, such as interests or affiliations.

There is a strong central group of players who were attached to both events and helped to connect many others. A good way to see this more clearly is to use the interactive “drag and fix” function of this D3.js visualization. I suggest the following: double-click on the large “aeaweb” node until you see a black outline appear (patience, it sometimes takes a couple of tries). The node is now “fixed” and can be dragged with your mouse to a position of say, a half-inch from the top of the map. Then, once things stop moving so much, do the same with the “impact_converge” node, dragging it to a similar distance from the bottom margin. As things begin to settle, you will see, in the middle are between the two fixed points, a set of influential “connectors” spanning both worlds.

 

The second visualization comprises participant who used only the IMPCON meeting’s hashtag and handle. This therefore is a closer look at the connections between those who identified in particular with the Impact Convergence event. Again, using the interactive feature, you can identify as set of influencers/connectors, this time, perhaps, separating the “impact_converge” and “svtgroup” nodes, or perhaps John Gargani’s node (John was just finishing his term as the AEA President).

I have posted this as an illustration of the power and potential of both social network analysis and D3.js visualizations for analysis and evaluation. Many other network analyses and interactive visualizations are possible with this data, and I will post these as I and others interested in bringing together these worlds do more analysis.