NodeXL EventGraph: ACM CHI2011 – Twitter Connections

08MayMay 7, 2015 By Marc Smith

The ACM CHI 2011 Conference is taking place in Vancouver, BC.

May 10, 2011

May 8, 2011

These are the connections among the Twitter users who recently tweeted the word chi2011 when queried on May 8, 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 network map is here: www.flickr.com/photos/marc_smith/5701022497/sizes/o/in/ph…

Top most between users:
@chi2011
@msftresearch
@edchi
@jeffbigham
@landay
@evanmpeck
@acagamic
@clifflampe
@yardi
@eytanadar

Graph Metric: Value
Graph Type: Directed
Vertices: 421
Unique Edges: 3849
Edges With Duplicates: 324
Total Edges: 4173
Self-Loops: 0
Connected Components: 32
Single-Vertex Connected Components: 29
Maximum Vertices in a Connected Component: 384
Maximum Edges in a Connected Component: 4146
Maximum Geodesic Distance (Diameter): 6
Average Geodesic Distance: 2.553249
Graph Density: 0.022587943

NodeXL Version1.0.1.167

Data set in NodeXL format.

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.

Mapping the Twitter networks of #CHI2010 with NodeXL

12AprMay 7, 2015 By Marc Smith

The CHI 2010 conference is well underway in Atlanta this week and lots of people are tweeting the #CHI2010 hash tag. An updated NodeXL map of the connections among people who use the #CHI2010 tag is below:

This is the network map of follows, replies, and mentions relationships among Twitter users who mentioned “CHI2010” as of April 12, 2010 scaled by number of followers.

This chart is a log/linear plot of followers (X) over tweets (Y).

Some people tweet a lot in order to get lots of followers, while others can tweet only modestly and gain a vast following. On a log/log scale this looks like:

The log scales are mislabeled (it is a known, pending work item!), but the pattern is clear: Tweet more and get more Followers. Some people break from the pack, of course, leading or lagging the core population. The tooltip is the latest tweet from the most followed Twitter author in the population.

Conference: NodeXL and Social Media Networks tutorial at CHI 2010

11AprMay 7, 2015 By Marc Smith

If you are attending the CHI 2010 conference in Atlanta and are interested in social media network analysis, consider attending this tutorial:

CN03: Introduction to Social Network Analysis
Time: Monday, 12 April 2010, 11:30 to 18:00
Organizers: Marc A. Smith, C.S. Ang, Derek Hansen, Panayiotis Zaphiris
Presenters: Derek Hansen, Panayiotis Zaphiris

Benefits
This course provides an overview of Social Network Analysis (SNA) and demonstrates through theory and practical case studies how it can be used in HCI (especially computer-mediated communication and CSCW) research and practise. This topic is of particular importance due to the popularity of social networking websites (e.g. YouTube, Facebook, MySpace etc.) and social computing. As people increasingly use online communities for social interaction, new methods are needed to study these phenomena. SNA is a valuable contribution to HCI research as it gives an opportunity to rigorously study the complex patterns of online communication.

Social network theory views a network as a group of actors who are connected by a set of relationships. Actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that are connected via various means in an online environment.
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