The network created by “Who follows who among the people who tweeted “#CHI2010”. Node size is proportional to total tweets. Generated with NodeXL
On October 29th, I will be offering a workshop in Mountain View, California on the application of social network analysis to the measurement of social media.
The workshop will run from 9m to 4pm and include hands-on exercises using real world social media data sets and the free and open NodeXL social network analysis add-in for Excel 2007. We will create social network metrics and visualizations from personal email, twitter, facebook, and message board records to reveal the broad outline of a community, its various kinds of leaders and active participants, major cliques and clusters, and pivotal events.
The workshop will make use of the NodeXL tutorial:
http://casci.umd.edu/NodeXL_Teaching
Registration at:
http://www.eventbrite.com/event/386418789
I will post the slides for those who cannot attend, but the live event will allow me to help those interested in learning how to visualize social media networks and generate and interpret social network metrics. What’s an “eigenvector centrality”? Come find out why that number highlights special people in a network and how to calculate it on your own network data sets. Find experts, identify the people who are most heavily connected, and key contributors. If you plan to attend, it would be great if you bring sample data: any edge list or matrix is fine. We can plot and measure sample data participants bring along with them.
I will demonstrate how to create twitter network maps like the one above which shows networks of follows connections among a group of people who tweeted the string “#CHI2010” (as returned by search.twitter.com). You can make your own twitter maps with NodeXL! Similar maps can be made with a user name. In the workshop we will be sure to make a twitter network for anyone there who tweets.
Upon completion of this workshop, participants will:
* be able to understand the basics of SNA, its terminology and background.
* be able to transform communication data (e.g. Twitter, email, flickr, message boards etc.) into network data.
* understand the different possible presentations of social networks, e.g. in a matrix or a sociogram.
* apply network metrics and visualizations to find clusters and key contributors in real world social media data sets.
* get familiar with the use of standard SNA tools and software in general and the NodeXL social network analysis add-in for Excel in particular.
* be able to derive practical and useful information through SNA analysis that would help design an innovative and successful online community.
Who should attend? People interested in community management, social media monitoring and marketing, knowledge management, collaboration and human resources, legal discovery, organizational behavior and management