In the sample map above for the term “CustServ” the visualization represents the connections among 1699 Twitter users over a 2-day, 21-hour, 48-minute period from Wednesday, 08 January 2014 at 02:53 UTC to Saturday, 11 January 2014 at 00:42 UTC.
The most central and possibly “influential” contributors to this discussion are:
Each of the six patterns is generated by the behavior of the individuals in the population.
In many cases the pattern you are is not the pattern you want to be.
This table describes each of the six patterns in terms of the difference between that pattern and the other five patterns.
Go down the rows until you find the pattern that most closely matches the network you currently have. Then work across the columns until you find the pattern that you want to become.
At the intersection is a color and a few ways to change and measure the transition from where you are to where you want to be.
A red square indicates an undesirable transition (who wants to become a divided discussion?). A yellow square is a low probability and difficult transition (it is hard to go from divided to unified). A blue square is a challenge but possible while a green square is a fairly easy transition to make.
Using this guide, you can plan a strategy for your social media engagement.
Imported Twitter networks now have an “in-reply-to tweet ID” column. This is a useful data element for building “paths” that capture how information flows through a network.
When you lay out each of the graph’s groups in its own box, you can now select how the boxes are laid out. Go to NodeXL>Graph>Layout>Layout Options in the Excel ribbon. (Thanks to Cody Dunne for this feature.)
The Check for Updates item has been removed from the Excel ribbon. NodeXL now automatically checks for updates once a day. Once this release is installed, NodeXL will automatically update itself when a new release is available. You will no longer have to manually download and install new releases. This release and those that follow will all be referred to as “NodeXL Excel Template 2014.” New releases will continue to have version numbers, but the numbers will be less important in light of the new auto-update feature.
If you use third-party graph data importers, such as the Social Network Importer for NodeXL, note that the folder where the importers are stored must be specified in the NodeXL>Data>Import>Import Options dialog:
If you use the NodeXL Network Server, an advanced command-line program that downloads a network from Twitter and stores the network on disk in several file formats, note that the program is no longer a part of NodeXL Excel Template. See “Using the NodeXL Network Server command-line program with NodeXL Excel Template 2014” at http://nodexl.codeplex.com/discussions/522830.
When a Twitter network is imported, the hashtags in the “Hashtags in Tweet” (or “Hashtags in Latest Tweet”) column are now all in lower case. Previously, identical strings with different case letters would be counted differently. This is no longer the case and the result is that terms that had been divided are now unified. These terms will now have higher values and there will be more diversity in the top ten list.
Thanks for using NodeXL and stay tuned for additional updates!
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.
The talk will focus on free and open tools for creating network maps and reports that can illuminate the landscape of social media.
The graph represents a network of 633 Twitter users whose tweets in the requested date range contained “sqlpass”, or who were replied to or mentioned in those tweets. The tweets in the network were tweeted over the 15-day, 2-hour, 48-minute period from Tuesday, 25 February 2014 at 00:26 UTC to Wednesday, 12 March 2014 at 03:15 UTC.
There is an edge for each “replies-to” relationship in a tweet, an edge for each “mentions” relationship in a tweet, and a self-loop edge for each tweet that is not a “replies-to” or “mentions”.
The graph’s vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm. The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.
Top 10 Vertices, Ranked by Betweenness Centrality:
This is a highly fragmented “Brand” network pattern with several prominent Broadcast hub and spoke structures centered around the most central participants: @thenextweb, @ow, @epro, @nicolasfordham, @gcouros, @malchord, @martinsfp, @plagia3, @k5launch, @taxion2.
I will talk about how anyone who can make a pie chart can now make these network maps and reports.