Lee Rainie, director of the Pew Internet Research Center was interviewed by Bob Garfield on OnTheMedia this week about the recently released report on mapping Twitter topic networks. The report found six distinct patterns of social media networks in Twitter: divided, unified, fragmented, clustered, and in and out hub and spoke patterns. They discuss the prospects for overcoming polarization in social media and the hopeful signs that many other forms of social network structures exist in addition to the divided network pattern.
Here is a nice way to display high resolution network maps: zoom.it!
This is a map of the connections among the people who tweeted the terms “FOCAS11” or “Aspen Institute” on August 2, 2011.
Connections among the Twitter users who recently tweeted the word Aspeninstitute OR FOCAS11 when queried on August 2, 2011, scaled by numbers of followers (with outliers thresholded). Connections created when users reply, mention or follow one another.
Layout using the “Group Layout” composed of tiled bounded regions. Clusters calculated by the Clauset-Newman-Moore algorithm are also encoded by color.
A larger version of the image is here: www.flickr.com/photos/marc_smith/6001893675/sizes/o/
Betweenness Centrality is defined here: en.wikipedia.org/wiki/Centrality#Betweenness_centrality
Top most between users:
Graph Metric: Value
Graph Type: Directed
Unique Edges: 649
Edges With Duplicates: 743
Total Edges: 1392
Connected Components: 81
Single-Vertex Connected Components: 76
Maximum Vertices in a Connected Component: 240
Maximum Edges in a Connected Component: 1284
Maximum Geodesic Distance (Diameter): 7
Average Geodesic Distance: 2.67739
Graph Density: 0.007023277
NodeXL Version: 188.8.131.52
More NodeXL network visualizations are here: www.flickr.com/photos/marc_smith/sets/72157622437066929/
NodeXL is free and open and available from www.codeplex.com/nodexl
NodeXL is developed by the Social Media Research Foundation (www.smrfoundation.org) – which is dedicated to open tools, open data, and open scholarship.
The book, Analyzing social media networks with NodeXL: Insights from a connected world, is available from Morgan Kaufmann and from Amazon.
NodeXL now (v.166) offers users a set of keyboard shortcuts that can speed up your routine network layout tasks.
After you click in the graph pane, a number of keyboard shortcuts are now available for functions that had previously been available in the visualization pane’s right-click menu. Now, you can press:
Ctrl+A to select all vertices and edges
Ctrl+V to select all vertices
Ctrl+E to select all edges
Ctrl+D to deselect everything
Ctrl+P to edit the properties of the selected vertices
Ctrl+C to save the graph image to the Windows clipboard
Ctrl+I to save the graph image to a file
Arrow key to move the selected vertices a small distance
Shift+arrow key to move the selected vertices a large distance.
(If you forget a shortcut, most of them are listed in the graph pane’s right-click menu.)
If you have any suggestions for other frequent tasks that could be accelerated with a keyboard command, please contact us on the NodeXL discussion board or here in the comments.
Using NodeXL, I have made several maps of social media networks of people talking about several topics of interest from current events to conferences I attend. You can find a collection of them on flickr.
I look at these images and look for differences in the number of big clusters: some images have a “double yolk” – that I propose is a necessary (but not necessarily sufficient) condition of defining a topic to be “controversial”. These two cluster networks have two well defined populations who lack much if any connection across the divide to the “other” side.
Some networks are highly populated but sparse, these are often the networks that form around brands where a central account tweets and is retweeted by many. But these many lack much connection to one another. So these brands form broadcast networks, not communities.
Some networks are dense single clusters with few if any “isolates”. Isolates are people who say a term, and thus appear in the graph, but have no connections (follows, replies, ore mentions) to anyone else in the graph (at least as observed and reported by twitter at that time). These dense clusters without isolates are topics where everyone is in-group. Examples, like “scrm”, are technical and business terms that identify medium sized populations with high levels of density.
Have a look and see what patterns you can find.
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