1. By Matt Bogard, M.S. Coordinator, Market Research Instructor, Department of Economics Western Kentucky University

2.  Social network analysis is focused on uncovering the patterning of people's interaction’-International Network for Social Network Analysis (link)link  Applications: Student Integration and Persistence Business to Business Supply Chains Terrorist Cells Twitter and Facebook

3.  “Social network analysis is much more than parsing a stream of tweets to see who’s flaming whom these days. At heart, it involves exploring the shifting web of relationships among people based on their profiles, interactions, and affinities.”-James Kobielus, Information Management  This presentation uses R to extract data from Twitter and uses R to visualize a network based on the last 100 tweets using a specified hashtag (from Twitter) (see Conway, 2009 for R code details)  Basic concepts from network analysis are introduced in this framework

4.  Betweenness Centrality: a measure of how often a node lies along the shortest path between other nodes in a network  Degree Centrality: the number of nodes a particular node is connected to  Eigenvector Centrality: proportional to the centrality of other nodes connected to a particular node in a network

5.  High Betweenness and Low Eigenvector Centrality: critical gatekeeper- (Conway, 2009) -these people may connect people to a network that otherwise would be isolated from the core  Low Betweenness and High Eigenvector Centrality: unique access to central actors (Conway, 2009) -these people may be at the heart of the core of a network, they are ‘well connected’

6.  R is a powerful robust statistical programming language used for Econometrics, Data Mining, Spatial Analysis, Bio-Informatics  Who Uses R? Google, Facebook, Merril Lynch, Merck Research Laboratories, Astra Zeneca, Bayer CropScience, Novartis Pharma, Bell Labs, Bank of Canada + many universities  UCLA has great online resources for more info about R : http://www.ats.ucla.edu/stat/R/ http://www.ats.ucla.edu/stat/R/

7.  Having formerly worked for University Libraries, I had the idea of looking at tweets for the upcoming American Library Association convention in 2010  As an experiment I blindly chose to look for the hashtag ‘#ala2010’ ( implying that I don’t normally follow tweets related to library topics, and without doing a search on twitter I had no idea if this hashtag was being used or by who)

8.  The network is depicted in the next slide and is constructed as follows:  Search the last 100 people using the specified hashtag  Get their ‘friends’  Identify common ‘friends’  Blue Dots = people using the specified hashtag  Grey Dots = people that have not used the hash tag but have 2 or more ‘friends’ that did

10.  By actually looking at twitter user profiles it turned out that most of these people were not ‘library related’  A commonality between users in this network was location- Spain  Looking at the extracted user data, a few library related users did appear- looking at their recent tweets indicated more common use of ‘#ala10’ vs. ‘#ala2010’

12.  Users in this network were more obvious  ALA JobLIST, ALALibrary, BookExpoAmerica  This illustrates the importance of searching tweets by the correct hashtag and the importance of using correct hashtags for maximum dissemination of your message

13.  The next slide presents a key actor analysis plot based on measures of centrality  A theoretical model implies a linear relationship between eigenvector centrality and betweenness (Conway,2009)  Departures from the line represent ‘residuals’  Larger residuals indicate larger values for centrality measures and are represented in the plot by font and color  Note: BookExpoAmerica and ALA Library have high measures of eigenvector centrality relative to other individuals- this is also evident in the original network structure

15.  Last 100 tweets as of April 16,2010  When looking at the next graphic:  Note the blue nodes ( indicating the use of #wku) that appear to be ‘connecting’ other #wku users from different parts of the network  Note the nodes that appear to be connected to a lot of users, that are also connected to more users

17.  Recall what you observed from the network, and see if it seems consistent with the key actor plot in the next slide  Can we identify key users that may be connecting people to WKU?  What kind of message are they sending?  What can we learn from this?  Is the network structure consistent over time?

19.  Borgatti,et al. Network Analysis in the Social Sciences. Science 323. 892 (2009)  Borgatti, Steve. Basic Social Network Concepts. 2002(presentation) Boston College  Conway, Drew. Social Network Analysis in R. New York City R User Group Meetup Presentation (link) August 6, 2009link  Conway, Drew. Optimal Terrorist Network Structures. (link) 2009link

20.  International Network for Social Network Analysis (link)link  Kobielus, James. Social Network Analysis: The Fuse Igniting Enterprise Data Warehouse Growth. It’s Planet Petabyte or Bust!’ Information Management. (link)link  R Statistical Package. http://www.r-project.org/index.htmlhttp://www.r-project.org/index.html  Thomas, Scott L. Ties Bind: A Social Network Approach to Understanding Student Integration and Persistance. The Journal of Higher Education, Vol 71. No. 5 (Sep-Oct., 2000) p.591-615.